Paul Sidwell

Alexis Michaud

Consonant shifts in

published or not. The documents may come

[1. "Germanic" type: devoicing of *voiced, aspiration of *voiceless]

Let us first examine the correspondences between the Phay 1 language (using vocabulary collected by Ferlus in Laos, west of Sainyabuli) and neighbouring languages: to the west, the language of the Mrabri, who are nomads of the western mountains of Nan province (Thailand), 2 and to the east, the language of the Khmu, more specifically the T'eng dialect recorded by Henri Maspero in Nghe-An (Vietnam) near the Laotian border, and the Tay Hay dialect recorded by Catholic missionaries in the province of Sam-Neua, Laos. 3 [1.1. Phay] First, we find correspondences between Phay aspirated initials and Khmu unaspirated initials [Table 1]. 4 1. At present, published data on the Phay language consists only of three vocabularies collected in Thailand in the province of Nan: Thin-saḥkaat, Thinnam-yaa, and Thin-'baan-toey, published alongside the Mrabri vocabulary in the Journal of the Siam Society (see [START_REF] Nimmanahaeminda | The Mrabri language[END_REF]). 2. An initial Mrabri vocabulary, under the name "Yumbri", was collected in 1936 by the photographer-reporter Hugo Bernatzik, working through a series of four interpreters. Understandably, it is not very useful (see [START_REF] Bernatzik | Die Geister der Gelben Blätter[END_REF]. The words cited here were collected by Kraisri Nimmanahaeminda (1963: 184) but there are still some obvious mistakes, e.g. when he asked for "dog" he was given "horse"; the two Thai words differ only in tone, and the Mrabri, whose language has no tones, speak Thai without tones. 3. In addition to the sources that I cited in my earlier article (1953: 123n1) [this volume: page xyz, footnote 3], it should be mentioned that Maspero's manuscript (T'eng) has now been published [START_REF] Maspero | Matériaux pour l'étude de la langue T'èng [Materials for the study of the T'eng language[END_REF]. As for W. A. Smalley's Khmu data, only a sketch grammar of Khmu has been published [START_REF] Smalley | Outline of Khmuʔ structure[END_REF]. I have also used a manuscript by R. P. Wauthier and John Subra (n.d.). 4. [Michel Ferlus (p.c. 2014) cites slightly different forms for some of these words in the Khmu dialect of the Luang Prabang area: 'breasts' (Table 2) is buʔ, with final glottal stop; 'woman' (Table 3) is cmkɨn (with a short vowel); 'forest, bush' (Table 4) is briʔ (with a short vowel and a final glottal stop); and 'egg' (also in Table 4) is kdoŋ (with a short vowel).]

tuber (edible) kʰwaːj kwaï

There is also a correspondence between unaspirated voiceless stops and voiced stops [Table 2].

[ This is clearly a shift of the Germanic type, whereby:

p > pʰ at the same time as

b > p t > tʰ d > t k > kʰ g > k [1.2. Samre]
The same shift is found in the language which will be referred to here as Samre, 6 spoken between Tonle Sap (the great lake of Cambodia) and the Gulf of Siam by people known as the Čɔng or Chawng of Thailand, the Pear of the Cardamom mountains, and the Suoi of Kampong Speu. The shift did not take place in the languages spoken across the Mekong, namely Köho, Biat and Bahnar, 7 all of which are as conservative as Khmu in this respect. Examples are shown in Tables 3 and4.

[ (Bahnar) 6. The data are from Baradat's manuscript (1941). For other sources, see Haudricourt (1953: 124n3) [this volume: page xyz, footnote 7] and the entries "Pear", "Chong" and "Samreh" in Shorto, Jacob et al. 1963. 7. For bibliographical references, see Haudricourt (1953: 124n2) Unfortunately, there is no convincing example of the expected correspondence between Khasi unaspirated voiceless stops and voiced stops in the conservative languages. However, there are traces of a third series of stops in Khasi. These stops are currently realized as voiced, but the fact that this series lacks a velar, g (a sound which is only found in recent borrowings), suggests that it originates in a former preglottalized series; a good example is the word for 'paddy', kba, which is ɓa in Mon and Bahnar, with a preglottalized ɓ. The second type of consonant shift (the Mon-Khmer type) occurs in Mon, and we have all the elements at hand to study it thanks to the excellent dictionary by [START_REF] Shorto | A dictionary of modern spoken Mon[END_REF]. Recall that this language is written in an alphabet of Indian origin which uses the Indian symbols for voiceless stops, (p, t, c and k) and for voiced stops (b, d, j [IPA: ɟ] and g). Mon preglottalized ɗ is written with the Indian letter for retroflex d [IPA: ɖ ] (transliterated ḍ ) and preglottalized ɓ by the Indian v with an added dot (transliterated ḅ). The first issue to be addressed is whether the confusion of voiced and voiceless stops is completed in Mon or if it is only an ongoing change. In modern Mon there is an opposition between two syllable prosodies: "head register", pronounced in a clear voice, and "chest register", pronounced in a muffled, breathy voice. The latter is characterized by a general relaxation of the speech organs, including the larynx, resulting in "breathy" voice and centralization of the vowel.

Recall that in Mon, words are monosyllabic or quasi-monosyllabic: the main syllable may be preceded by a reduced syllable with the same "voice register". Thus we find, in "head" register:

'to obtain' ket (written ket) 'side' həkoa 9 (written skay) and in "chest" register:

'to turn (tr.)' ket 10 (written get) 'cat' həkoà (written bagay). Shorto (1962: xi) tells us that in "chest-register" words k, c, t, p are partly voiced in initial position and fully voiced in medial position. In other words, ket 'to turn' is pronounced [kget], and həkoà 'cat' is pronounced [There is a typo in the original, where the word is written as kəkoa.] 10. [In the original transcription, the low (breathy) register was not indicated; it was clear from the heading. In the translation, this indication was added for all the Mon data in the article, using the standard transcription: a grave accent for low (breathy) register, and the absence of accent for high register.] dissociation between the voicing of the initial and the "head" or "chest" register of the syllable. Thus, we have, in head register:

[həgoà].
'widow' mai (written kmay) vs 'omens' hnoa 11 (written snay)

and in chest register:

'indigo' mài (written may) vs 'comb (of cock)' hnòa (written dnay).

[Since these words are identical in terms of initial consonants, the contrast between them has to be recognized as consisting in the voice register.] This influence of the "register", which, in synchrony, causes a difference in the tenseness or laxness of the vowel, is also observed in diachrony. Comparing quasi-homographs which, in writing, differ only in their initials, it can be observed that their actual pronunciation differs not only in register [i.e. phonation type and pitch] but also in vowel quality [ Ferlus has recently discovered a similar phenomenon in the Sô language, spoken in the vicinity of the city of Thakhek in Laos: words borrowed from Thai have retained their original vowel quality when the initial was voiceless, but their vowel quality has changed when the initial was voiced [ The Sô language is spoken as far east as the hills bordering the Gulf of Tonkin by the Van Kieu, the Khua, the Bru, the Pacoh, and the Katu. In these dialects, too, the consonant shift seems to have reached completion, with the confusion of two series of initials, voiced and voiceless, resulting in a split in the vowel system -a system that was already crowded, as it distinguished four degrees of aperture as well as length. Thus, according to Watson (1964: 136-139) Smalley (1964) provides some details on the Kuy dialects, spoken across the Mekong in Thailand and Cambodia, based on the unpublished notes of Richard Johnston. A total of 24 vowel phonemes are distinguished by quality and length (11 short and 13 long), but each can also be "breathy" or "non-breathy", which could yield a total of as many as 48 vowel phonemes. But in the present state of the language, the voiced stops and the voiceless aspirates do not occur before breathy vowels. The voiced stops are former preglottalized stops that behave like voiceless stops [in that they yield a "non-breathy" register], and the aspirates are loans from Tai, and proto-Tai had only one series of aspirated consonants, which were voiceless. In his discussion of the rendering of Kuy by means of the Thai alphabet, Smalley explains that breathy voice quality tends to cause a preceding stop to be perceived as aspirated (there is no phonological opposition with a true aspirate in that context), and that speakers of the language, who are bilingual, transcribe stop consonants followed by breathy vowels 20 using the symbols for aspirated consonants, adding the diacritic for low tone. 12a].

20. [The original reads "followed by breathy consonants" instead of "followed by breathy vowels".] 21. See Shorto et al. (1963: 14), "Niakuol" entry, and Chintana (1958: 34-45).

[Table 12a The former glottalized series retained its voiced character [and thus remained distinct from the other series of consonants: see [2.5. The Khmer shift]

The type of consonant shift which we have observed in Mon and Sô is reminiscent of that found in Khmer, presented implicitly by Martini (1946: 112-131) when he pointed out the relationship between the traditional orthography and the pronunciation.

The essential difference between the type of onset consonant shift found in Khmer and that found in Mon and Sô is that in Khmer, as also in the languages to the west (Chong and Samre) and east (Stieng and Köho), there is no trace of the existence of a former preglottalized series: the initial consonants in the two diagnostic examples 'two' and 'water' evolved like ordinary voiced stops [i.e. they became voiceless]. The consequence of this gap was that nothing prevented the former voiceless stops from becoming more tense, more glottalized, until they finally became voiced [and preglottalized] when followed by a vowel. Examples are shown in Table 13a.

[Table 13a The consonants of the former voiced series are now voiceless, as shown in Table 13b.

[ According to [START_REF] Martini | Aperçu phonologique du cambodgien [A phonological outline of Cambodian (Khmer)][END_REF] there are 32 vowel phonemes in present-day Khmer, placing this language between Pacoh (30 vowels) and Bru (41 vowels) in terms of richness of the vocalic inventory. According to [START_REF] Henderson | The main features of Cambodian pronunciation[END_REF], the language has two vowel registers, as in Mon.

[

"Far-Eastern" type: initial merger and tonal split]

There is obviously a close relationship between the Mon-Khmer type of consonant shift, where confusion of initials leads to two voice registers that affect the quality of vowels, and the shifts observed in tone languages of the Far East (see the discussion of the two-way split of tonal systems in Haudricourt 1961 [translated in this volume]). One wonders if these two voice registers are interpreted as pitch differences only in languages that already have tones, in which case the observations of G. H. Luce on Riang and of K. G. Izikowitz on Lamet should rather be interpreted in another way. Luce and Izikowitz were conscientious observers and were familiar with tone languages, but were not professional phoneticians. It is not unlikely that Riang and Lamet are actually voice register languages rather than tone languages. 25 Ferlus tells me there is a Lamet dialect with "diphthongization" and hence probably with voice register.

24. [In the original article, the transliterations of Written Khmer appear as bi for 'two' and dik for 'water'; the pronunciation indicated for 'water' is tik.] 25. [This conjecture was confirmed since. On Riang, see [START_REF] Shorto | Riang-Lang vocabulary: Compiled from the materials collected by[END_REF] and [START_REF] Shintani | The Riang language[END_REF]. The synchronic facts for Lamet are more complex [START_REF] Lindell | Two dialects of the Rəmeet (Lamet) language[END_REF]) because some dialects have become predominantly tonal (Ferlus, p.c. 2016).]

On the other hand, Danaw, spoken in Burma east of Inle Lake, which was not available to [START_REF] Shafer | Etudes sur l'austroasien [Austroasian studies[END_REF], but was recently rediscovered by [START_REF] Luce | Danaw, a dying Austroasiatic language[END_REF], is indeed a tone language. Comparing Danaw with Riang, Luce observed that the low tone of Riang corresponds to the high falling tone of Danaw, but that the high tone of Riang corresponds to two distinct Danaw tones: low falling and high level, the latter for words which in Riang retain a final glottal closure that has disappeared in Danaw. Final glottalization is precisely what gave rise to the sắc-nặng tone in Vietnamese [START_REF] Haudricourt | De l'origine des tons en vietnamien [The origin of tones in Vietnamese[END_REF]]. Examples of the correspondence between Danaw High level and Vietnamese sắc-nặng tones are shown in Table 14.

[Table 14 Finally, Danaw has also preserved the former preglottalized stops as a

[distinct] series, now realized as voiced stops, e.g. 'paddy' ba, 'bamboo shoot' tubong.

In Vietnamese and Muong, the merger of initials occurred after the development of tones. 27 The voicing contrast is perhaps maintained in the 26. [Haudricourt's phonetic rendering of the Vietnamese examples is: ćɔ, ćei, la, ṭaːy, ha, ɣaːw, luo, lɔ.] 27. See [START_REF] Haudricourt | Bipartition et tripartition des systèmes de tons dans quelques langues d'Extrême-Orient [Two-way and three-way splitting of tonal systems in some Far Eastern languages[END_REF], pp. 172-173, and p. 165, note 3. To the sources cited in Haudricourt (1953: 123, note 2) must now be added the word lists for Poong, Arem, Ruc and May published by Vương Hoàng-Tuyên (1963: 196).

mountainous dialects, such as Tay Poong, 28 Khong-Kheng, Hung etc.

[Table 15].

[Table 15 On the other hand, the single Muong series k, c, t, p corresponds to two series in Vietnamese: (i) a series of stops, some of which are preglottalized: k-, c-, ɗ-, and ɓ-, written as c, ch, đ and b; (ii) a series of voiced spirants: ɣ, ʒ, ð, 29 and ʋ, written as g, gi, d and v. This second series could be explained 28. [In spite of its name, Tay Poong is not related to Tay (Tai-Kadai family): it is a language of the Vietic group of the Mon-Khmer family. From a general point of view, we can classify the consonant shifts observed above as follows.

The correlation that contrasts two series of stops (fortis voiceless vs. lenis voiced) can evolve in either of two ways.

The first type of evolution originates in a reinforcement of the fortis series: the contraction of the laryngeal-oral muscles increases the air pressure inside the mouth during the interval when the stop is maintained; the air escapes with plosion, turning the voiceless stops into aspirated stops. The voiced stops may then lose their voicing without danger of lexical confusions. This is the "Germanic" type of consonant shift.

The second originates in a weakening of the lenis series: the relative laxness of the laryngeal-oral muscles is prolonged from the articulation of the initial consonant into that of the following vowel. The relaxation of the larynx lets breathy voice come through and lowers the pitch of the voice, while the relaxation of the muscles of the mouth results in a "lax" vowel quality.

When this process takes place in languages without tones, it is the difference in vowel quality that eventually becomes distinctive. A correlation of consonants disappears as a correlation of vowels appears, decreasing the number of consonants, while increasing the number of

[This hypothesis was confirmed by later comparative research about

Vietnamese: see the study of the spirantization of medial consonants by [START_REF] Ferlus | Spirantisation des obstruantes médiales et formation du système consonantique du vietnamien [The spirantization of medial obstruents in Vietnamese, and the development of the Vietnamese consonant system[END_REF].] vowels. On the other hand, when this happens in a tone language, it is the change of register that becomes distinctive: the correlation between consonants disappears and a tonal correlation appears, decreasing the number of consonants while causing a split of the tone system. These two subtypes can be superimposed, as in the tonal southern Tai 31 languages (Thai, Lao, and Saek). These languages were already tonal when the onset consonant shift took place, but the shift was carried out on a Mon-Khmer substratum and adstratum: the breathy character of the vowel that followed the former voiced stops became aspiration, such that b, d, ɟ and g became pʰ, tʰ, cʰ and kʰ. 32 On the surface, this change seems to be exactly the inverse of the "Germanic" shift with which we started. I believe I have shown that it is fundamentally the same phenomenon, and that the differences between the phonological systems of the languages at issue and their historical contact situations suffice to explain the different outcomes.

Comments

This article builds on "Two-way and three-way splitting of tonal systems in some Far Eastern languages" (this volume), which had been published four years earlier. In Vietnamese, the voicing opposition on initial consonants transphonologized to new tonal oppositions, resulting in an increase in the 31. [Haudricourt's "southern Tai" (original French: "langues Thai méridionales") corresponds to Li [START_REF] Li | A Handbook of Comparative Tai[END_REF] Southwestern Tai. Haudricourt uses the term "Thai" to refer to a set of languages that by and large corresponds to Li's Southwestern Tai and Central Tai, exclusive of Northern Tai (which Haudricourt calls "Dioi", or "Zhuang", after the Chinese ethnonym Zhuàng 壮

). The more modern term "Tai" is used in the present translation; "Thai" refers to the national language of Thailand, also known as "Siamese". For more details, see "On the reconstruction of initial consonants in monosyllabic languages: the case of proto-Tai" and "What geography, ecology, and semantics can tell us about the origin of the Tai peoples", in this volume.] 32. A necessary requirement for this change is the existence, prior to the change, of a series of voiceless aspirates. This condition was met in Tai as well as in Chinese. In Mon-Khmer languages, on the other hand, the sounds transcribed with the series of letters used for voiceless aspirated stops in Indic languages are in fact clusters formed of a voiceless stop followed by h [so the condition for the reinterpretation of breathiness as aspiration was not met in Mon-Khmer].

number of lexical tones. The present article brings out other evolutionary paths followed by this opposition in different languages.

Later work has confirmed the links between the type of change which Haudricourt calls "Far Eastern" (split of the tone system) and that which he calls "specifically Mon-Khmer" (split of the vowel system) [START_REF] Ferlus | Formation des registres et mutations consonantiques dans les langues mon-khmer [Development of phonation-type registers and consonant shifts in Mon-Khmer languages[END_REF]). These two types share a common initial stage from the phonetic point of view: the relaxation of the larynx. At that stage, the phonetic cues to this opposition include, in addition to phonation type proper, some differences in pitch, as well as differences in vowel articulation, as was already noted for a conservative variety of Khmer by [START_REF] Henderson | The main features of Cambodian pronunciation[END_REF]. At a later stage, one or the other of the cues becomes dominant: this is where the evolution branches into the "Mon-Khmer" type (where vowel quality stabilizes as the new distinctive property) and the "Far Eastern type" (where the distinctions become tonal).

An extension of the general theory of the changes associated with "registrogenesis" has been proposed in a study of vowel changes from Old Chinese to Middle Chinese (Ferlus (2009: 192).

  8

  8. [Throughout the article, Modern Mon forms from Shorto's Dictionary of Modern Spoken Mon (1962) have been added after Written Mon forms. In these modern forms, Shorto's notations for the preglottalized stops have been modified from b and d to the IPA symbols, ɓ and ɗ.]

  [2.4. Chaobun (Nyahkur)] This explains the state of affairs found in the Chaobun [Nyahkur] dialects of Thailand, of which we only have transcriptions in the Thai alphabet: 21 [Table

3 [ Table 1 .

 [1 Aspirated stop in Phay vs. unaspirated in Khmuic languages.]

	gloss	Phay	Khmuic languages 5
			Mrabri	T'eng	Tay hay	Khmu
	three four fruit bush leech eight six to do/make to sell hand bat (animal) ntʰəːl pʰɛʔ pʰoːn pʰlɛʔ pʰlɔːm tʰiʔ tʰuol tʰɛːŋ tʰəːc ntʰiː tongue ntʰaːk fish kʰaː arrow kʰam child kʰuon thunder kʰəːl	pɛh poːn ti tuon tiː kaː kɔːn kɯl	pleʔ tɛːŋ tiː tɯər k aː kam kɔːn kur	pleʔ plɯəm tɛːŋ teːt tiʔ ntɯəl n t aːk k aʔ	k aʔ

Table 2 .

 2 Voiceless unaspirated stop in Phay vs. voiced stop in Khmuic languages.]

	gloss	Phay	Khmu dialects
			Mrabri	T'eng	Tay hay	Khmu
	bear two ash alcohol breasts to cut to eat coconut tree	piek pie puo puc poʔ pok pəŋ plaːw	bɛːk bɛːr boʔ baŋ	baːr bɔʔ buic buː bɔk blaːw	kbaːr bɔh buːc buː
	5. [In the original article, this column header reads "Khmu dialects", but the preceding text indicates that Mrabri and possibly Tay Hay are separate languages; this is consistent with current classifications, which subgroup both (and Phay) as "Khmuic", not "Khmu". See Sidwell (2009).]

Table 3 .

 3 Correspondence between Samre aspirated stops and unaspirated stops in more conservative languages.]

	gloss	Samre dialects	conservative languages
	three four bush leech fruit sky	pʰai, pʰei, pʰeːk pʰoːn, pʰoun pʰliːm, pʰleːm pʰliː pʰliɲ	pɛː (Köho, Biat, Boloven) puon (Köho, Biat, Boloven) pləːm (Köho) plaj (Köho, Boloven) pleɲ

  Table 6 provides a summary.

		register	gloss	Written	Modern Mon
				Mon	phonemic	phonetic
	monosyll. quasi-monosyll.	"head" "chest" "head" "chest"	to obtain to turn tr. side cat	ket get skay bagay	ket ket həkoa həkoà	ket k get həkoa h ə g o à

[Table

6

. Mon data illustrating allophonic variation of voiceless stops depending on their initial or medial position. 'Monosyll.' = monosyllable.] Should we conclude that the voiceless/voiced opposition is still [phonemically] distinctive and that the "voice register" is merely its phonetic consequence? No, because for nasals we have examples of 9.

Table 7 ]

 7 .[Table7. Modern Mon pronunciation of identically written vowels in "head" and "chest" registers.]

	voice]

"Head register" [i.e. modal voice] "Chest register" [i.e. breathy taoh fault duh tùh [2.2. Sô]

Table 8

 8

]. 11. [The transcriptions hn (etc.) represent unvoiced nasals (IPA n̥ , etc.)] 12. [Transliterated by Haudricourt as yat ga.] 13. [Transliterated by Haudricourt as bāk.]

[

Table 8 .

 8 Vowels in breathy-register Sô borrowings of Thai origin, and in the corresponding Thai etyma (with voiced initials in Old Thai).]

	gloss	Lao	Thai 14	S ô	[Thai
	big knife harrow slowly time price one million copper gong gently bar of wood expensive to be waning	pronunciation pʰaː kʰaːt saː veːlaː lakʰaː laːn thɔːŋ kʰɔːŋ kʰɔːj thɔːn pʰɛːŋ mɛːn	brā 2 grāt jā 2 velā lahgā 1 lān dåṅ gåṅ gåy dån beːṅ meːn 1 15	pronunciation prɛa krɛat cɛa veːlɛa lakɛa lɛa tɔaŋ kɔaŋ kɔaj tɔan peːŋ meːn	script] พร า คราด ช า เวลา ราคา ล าน ทองแดง ฆ อง ค อย ท อน แพง แม น
	(moon)
	gloss	Other Mon-Khmer	Sô pronunciation
	duck violin banana	ada (Köho), daː (Boloven) draw (Mon) 16 brāt (Mon)	tɛa trɔa

hɛːm reːm zeːm แรม Words of Mon-Khmer origin show the same evolution [

Table 9]. [Table 9. Vowels in breathy-register Sô words of Mon-Khmer origin, and in cognates (with voiced initials) in conservative Mon-Khmer languages.] priat salt boh (Köho) pɔh belly buṅ (Mon) puŋ On the other hand, former voiced preglottalized initials did not lose their voicing; they are reflected by plain voiced stops [Table 10]. 14. [This column reproduces Haudricourt's transliteration of Thai.] 15. [The meaning in contemporary Thai is 'accurately, precisely, truly'.] 16. [Transliterated by Haudricourt as drå.]

  there are at present 30 vowel phonemes in Pacoh, and the Millers and R. Phillips 18 found 41 vowel phonemes in Bru.Concerning high vowels, Ferlus observed that for the Sô of Thakhek, there is only a difference in register, while for Bru in Vietnam, in the transcriptions of J. and C. Miller 19 we find diphthongs after former unvoiced initials [Table11].[Table 11. Sô and Bru data.]

	Following formerly voiced series:
	pig village brother-in-law pair	aliːk viːl anjiː kuː	aliːk viːl anjiː (uncle) k uː (Lao: kʰuː < proto-Tai *guː)
	[2.3. Kuy]
	Following formerly voiceless series:
	Sô (Ferlus p.c.)	Bru (J. and C. Miller n.d.)
	hand to take, to obtain dead five to see four knife name of ethnic group bruː atiː ʔiːt kuciːt sɯːŋ hɯːm puːn acuː		atei ʔeit kuceit səɯŋ həɯm poun acou brou
	17. [In Haudricourt's notations, consonant preglottalization is indicated with an apostrophe, hence Mon 'ba, t'bang, 'dāk, 'duṅ and Bahnar 'baar, t'bang,'daak.] 18. [Possibly a pre-publication version of Phillips et al. 1976.] 19. [Haudricourt probably had access to J. Miller (n.d.) and J. and C. Miller (n.d. [c. 1963]). See references.]

  . Aspirated initials in Chaobun vs. voiced initials in Written Mon.

	gloss	Chaobun dialects 22	M o n 23
	belly wife rain to bind duck water turtle rope liver forest	phung [pʰùŋ] phau [pʰràw] phəːy, phləːy [pʰɛj]~[pʰəj] thak [tʰàk] thaː, thia [tʰàː] thi [tʰwìːˀ] choːk [sòːk]~[cʰòːk] khi [kʰrìh] kheːp, khrəːp [kʰrəp]	buṅ [pɜŋ] brau [prɛà] bray [pròa] dak [tɛàk] ada [ətɛà] gwi (dwi) [kwìˀ] juk [cɜk] gruih [krɜh] gruip [krɜp]
	Thus former voiced initials are represented by aspirates, while former
	voiceless initials are unchanged [Table 12b].
	[Table 12b. Voiceless initials in Chaobun vs. voiceless initials in Written Mon.]
	gloss	Chaobun dialects	Mon
	ground, earth hand tongue cooked rice three four buffalo fish child	tiʔ [tiːˀ] təy [tɛj]~[təj] taːk [ntaːk] pong [poːŋ] pi [piːˀ] pan [pan] pieng, plieng [priɛŋ] kaʔ [kaːˀ] kuon [kuan]	ti [tɔeˀ] t ay [toa] latāk [kətaik] puṅ [pɜŋ] pi [pɔeˀ] pan [pɔn] preṅ [preaŋ] ka [kaˀ] kon [kon]

  Table 12c]. 22. [Nyahkur data from Ferlus's unpublished field notes are provided in square brackets in the Chaobun column; the grave accent indicates breathy voice.] 23. [Modern Mon, from Shorto 1962, has been added after Written Mon forms.] [Table 12c. Voiced initials in Chaobun vs. preglottalized initials in Written Mon.]

	gloss	Chaobun dialects	Mon
	two water village	baː daːk dong	ḅā [ɓa] ḍāk [ɗac] ḍuṅ [ɗɜŋ]

  . Voiceless stops in Written Khmer and conservative languages vs.

		preglottalized stops in present-day Khmer.]
	gloss	Khmer	pronunciation	other Mon-Khmer
	three four earth hand	orthography pi puon ti tai	ɓei ɓuon ɗei ɗai	Köho pɛ pɔn tiah ti	Stieng pai puon teh ti

Table 13b .

 13b Voiced stops in Written Khmer and conservative languages and voiceless stops in present-dayKhmer.]

	gloss two forest thigh water duck to think	Khmer orthography pronunciation bīr piː brai prei bhlau pʰləw dịk 24 tɨk da tea git kit ~ kɨt	other Mon-Khmer baːr (Köho) bri (Köho) bluː (Biat) daːʔ (Köho) da (Stieng), ada (Köho) git (Köho 'to know')

  . Correspondence of Danaw high level tone with the tones sắc and nặng (B1 and B2) ofVietnamese.]

	gloss	Danaw	Vietnamese 26	Vietnamese
	high level tone tso tsi la ple to open (mouth) ha dog louse leaf fruit husked rice ko thread lu pot lo	[cɔ B1 ] [cɤj B1 ] [la B1 ] [ʈaj B1 ] [ha B1 ] [ɣaw B2 ] [luə B2 ] [lɔ B2 ]	orthography sắc tone (B1) chó chấy lá trái há nặng tone (B2) gạo lụa lọ

  In Muong, the stops resulting from the merger of voiced and voiceless stops have remained voiceless, and preglottalized stops have remained unchanged.In Vietnamese, preglottalized stops have become nasals, with the same high series tones as the former voiceless nasals. Thus, Muong ɗam 'five' corresponds to Vietnamese năm, which is homophonous with năm 'year'[illustrating the merger of the former preglottalized stops with the former voiceless nasals]. (That the word for 'year' used to have a voiceless nasal is shown by the Mon cnam > hnam.)

		. Possible preservation of an old opposition of voice in Tay Poong as
		compared with Muong, 17 th -century Vietnamese (VN), and modern VN
		dialects.]
	gloss sky moon	Tay Poong Muong 17th-c. VN southern VN bləj tləj blời trời plɔŋ tlaŋ blăng trăng	northern VN giời giăng

  It is also known asPoong.] 29.[Haudricourt's notation is γ , ẓ, ź and ʋ, i.e. suggesting ʑ or ʐ as the pronunciation of Vietnamese d. Comparative evidence, and Vietnamese borrowings into neighbouring languages, support the view that at the time when the Vietnamese orthography was designed, the phoneme written as d was a dental spirant, written as δ by Ferlus 1982 (see also[START_REF] Ferlus | Les hypercorrections dans le thổ de Làng Lỡ (Nghệ An, Vietnam) ou les pièges du comparatisme [Hypercorrections in Làng Lỡ Thổ (Nghệ An, Vietnam): the pitfalls of historical-comparative phonology[END_REF]. The closest equivalent to this sound in the International Phonetic Alphabet is ð. On spirants, see the Glossary. On the Vietnamese alphabet, see[START_REF] Haudricourt | L'origine des particularités de l'alphabet vietnamien. Dân Việt-Nam 3. 61-68. Translated as: The origin of the peculiarities of the Vietnamese alphabet[END_REF].] by the existence of an rprefix found in certain dialects, such as Ruc and Arem [Table16].30

	[Table 16. Correspondences between Vietnamese, Muong and other Mon-Khmer
	languages, suggesting the presence of an r-prefix in these words.
	"Viet."=Vietnamese, "Viet. spell."=Vietnamese spelling.]
	Viet. Viet. spell. Muong [Conservative Mon-Khmer languages] ɣa gà ka rəka (Ruc), ləkɛ (Arem) husked rice ɣaːw chicken gạo kaːw rəko (Ruc, May) rəkaw (Palaung) cotton ʋaːj vải paːj kupal (Ruc), kəpai (May), karpās
	(Khmer)
	[4. Conclusion]


Alexis Michaud

The purpose of this paper is to bring out the panchronic conditions governing the evolution from nasal consonants to oral consonants. In languages with nasal vowels, there can be (i) a carry-over of orality into a following consonant: *am>abm (and *am>am), or (ii) an anticipation of orality into a preceding consonant: *ma>mba (and ma>ma).]

[Introduction]

The purpose of this paper is to determine the panchronic conditions governing the evolution from nasal consonants to oral consonants. A case which is famous among sinologists is that of Kan-on, the Chinese dialect of ancient Japan, where all former nasal consonants became voiced stops: m>b, n>d, ɲ>z, ŋ>g, so that this dialect lacks initial nasal consonants altogether (Karlgren 1940:67).

In the language families that are best understood at present, namely, Indo-European, Semitic and Finno-Ugrian, nasal consonants are diachronically stable. These consonants are present in almost all languages. Under which exceptional conditions can they become altered? In the case André-Georges Haudricourt 2 of Chinese, it has been hypothesized (for quite some time now) that there must have been an intermediate stage when these consonants were prenasalized: m b, n d, etc. Phonemic prenasalized stops are actually fairly widespread in Oceania and in Central and South America [so that relevant information about these phonemes and their evolutionary potential can be obtained by taking a close look at Oceanian and Amerindian languages; in turn, this information can shed light on the developments that took place in the history of the Kan-on dialect].

In Oceania, Jean-Claude Rivierre, in his study of the dialects of the Numea language (New Caledonia), found the correspondence shown in Table 1 between the conservative dialect of Goro and the evolved dialect of Unia. In the Goro dialect nasal consonants can be followed by either oral vowels or nasal vowels. In the Unia dialect, there is no opposition of nasality among vowels after a nasal consonant: the presence or absence of nasality on the vowel is not phonologically relevant. The interpretation of these comparative data is that nasal consonants that used to be followed by an oral vowel became prenasalized stops in the Unia dialect. The distinctive feature which used to be carried by the vowel (oral vowel vs nasal) was transferred onto the second half of the preceding consonant.

[This sheds light on the conditioning of the sound change:] One of the conditions of the consonant shift whereby nasal stops become prenasalized stops is thus the presence of nasal vowels in the language. This condition is likewise fulfilled in the Otomi language of Mexico. Consider the dialectal data in Table 2 (from Bartholomew 1960: 327).

[Table 2 Nasal consonants followed by an oral vowel have been preserved in San Felipe, but have become prenasalized stops in San Gregorio.

A similar phenomenon took place in some Austroasiatic languages of the Indochinese peninsula. In Jeh (a language of Vietnam), there are nasal vowels in certain words with initial hor ʔ-. In Halang, another language of Vietnam and a neighbour of the Jeh language, the final consonants -m, -n,ɲ and -ŋ are only found after vowels which are themselves preceded by a nasal consonant; in other contexts, the final consonants are -bm, -dn, -ɟɲ and -gŋ, for example naŋ 'to hear' and hagŋ 'sensation of scalding' [START_REF] Gradin | Consonantal tone in Jeh phonemics[END_REF]: 46, Cooper & Cooper 1966: 94). Table 3 provides examples from Chewong, a Semang dialect spoken in Malaysia.

[Table 3 From the data in Table 3, it can be concluded that -m, -n, -ɲ and -ŋ were preserved when they were preceded by a nasal vowel -whether the nasality of the vowel was due to the influence of a preceding nasal consonant, or to a feature of nasality that the vowel inherited from a former nasal initial when that nasal initial turned into a glottal hor ʔ-. On the other hand, when the vowel was oral, there was a carry-over 1 of orality [into the beginning of the following nasal consonant], yielding -bm, -dn, -ɟɲ and -gŋ.

Prenasalized consonants are not very common across languages; they can evolve rapidly into (oral) voiced stops. Such is the case in Siouan languages, where the correspondences in Table 4 2, so that] the consonant shift from nasal consonants to oral consonants took place. This sheds light on the origin of the Kan-on dialect which was introduced into Japan, and which lacked initial nasal consonants: it was a learned pronunciation that had undergone a consonant shift caused by the influence of a vernacular pronunciation.

As for the languages that are reported to have lost nasal consonants altogether, as has been claimed for some Amerindian languages (Hockett 1955: 119), I wonder if this conclusion is not simply due to an incomplete inventory of the language's lexicon. Supposing that nasal vowels in these languages are not widespread, the items that preserve nasals may be rare words which escaped the scrutiny of the investigators. Nasal and prenasalized consonants (1970) 5 consonant shifts at issue is not so much the frequency of nasal vowels as their presence in the system, and their occurrence in contact with nasal consonants.

Discussion

[This is a transcript of the discussion that followed Haudricourt's talk. It is part of the original publication.]

T. Akamatsu:

I

R. Gsell:

Mr. Haudricourt's brilliant paper could lead to the recognition of three degrees of consonantal nasality (plus a zero degree corresponding to fully non-nasal consonants):

-zero degree: b, d, etc.; or p, t, etc.

-1 st degree/reduced degree: m b, n d (prenasalized stops)

[The original mistakenly indicates "open vowels" instead of "open syllables".]

André-Georges Haudricourt

6

-2 nd degree/full degree: m, n, etc. These are the ordinary nasals.

When nasal vowels follow these nasal consonants, they are definitely nasal.

-3 rd degree/strong degree: this degree of nasality is both consonantal and vocalic. Following a 3 rd -degree nasal consonant, the vowel has to be realized as nasal, so that nasality over the vowel is not distinctive.

In fact, neither is there a contrast of nasality on the consonant as distinct from the vowel, since the C ̃Ṽ complex makes up an undecomposable whole.

Systems that have all three degrees appear to be rare; they are found in some Melanesian and Amerindian languages. When consonant shifts affect nasal consonants, it should be possible to explain them as transphonologizations whereby the degrees 0, 1 and 2 change to 1, 2, and 3, respectively (i.e. a reinforcement of nasality) or conversely 1, 2 and 3 change to 0, 1 and 2 (i.e. a reduction of nasality).

I would love to hear Mr. Haudricourt's opinion on this subject.

Answer by A.-G. Haudricourt:

Nasality being a marked feature, all the examples that I provided are instances of the latter process: a reduction of nasality. The opposite case, whereby mb would change to m and nd to n, only exists in some intervocalic positions in Indo-European languages (from the Romance and Germanic groups) that allow geminated consonants [i.e. the change is from mb to mː, and from nd to nː].

Comments

This short article brings attention to the wide range of transfers of nasality attested across languages, and illustrates the panchronic approach to historical phonology: building on observations about distributional constraints on nasal phonemes in order to understand their evolutionary potential.

Shortly after the publication of this conference paper, supporting evidence came from report of a change in progress from *CVNV to CNV and finally to CṼ in the Kwa branch of the Niger-Congo family. In Gwari, "*kNU and *gNu are already pronounced [kŋu] and [gŋu], where the nasal release is not particularly pronounced. (...) /sNi/ is pronounced [si ̃]" (Hyman 1972: 176).

On the other hand, the reservations expressed in both studies (Haudricourt 1970 and[START_REF] Hyman | Nasals and nasalization in Kwa[END_REF]) about the possible range of historical evolutions have since been shown to be mistaken. Haudricourt was aware of the interest of Siouan languages for a panchronic study of nasality, but he voiced doubts about the possibility of a system that has no nasal consonants: "As for the languages that are reported to have lost nasal consonants altogether, as has been claimed for some Amerindian languages (Hockett 1955: 119), I wonder if this conclusion is not simply due to an incomplete inventory of the language's lexicon." (last paragraph of the paper) and Hyman considered that "...it seems much more natural to speak of the nasality as having shifted from the consonant to the vowel, i.e. [CNV] becomes [CṼ]. The reverse (with denasalization of V) would be very strange indeed." (Hyman 1972: 176) Contrary to these views, it is now established that consonant nasalization from a following nasal vowel is an attested historical change in Siouan languages -and thus a panchronic possibility (seminal research on this topic includes Aubin 1975, Boyle 2007, and[START_REF] Carter | Comparative Siouan Dictionary (computer database)[END_REF] in preparation; on other Native American languages that are relevant to this topic, see [START_REF] Rose | A typological overview of Emerillon, a Tupi-Guarani language from French Guiana[END_REF], Wetzels 2009[START_REF] Epps | A Grammar of Hup[END_REF]. A correspondence such as Winnebago /-paña/ vs. Chiwere /-bla/ ('ten') looks like another case of transfer of nasality from a TN-onset, suggesting a reconstruction as *-pna. But advances in the reconstruction of Proto-Siouan lead to the conclusion that the change was in fact the reverse. The word for 'ten' is to be reconstructed as *-wra, not *-pna [START_REF] Michaud | Historical transfer of nasality between consonantal onset and vowel: from C to V or from V to C?[END_REF].

These findings allow for further progress towards the objective stated by Haudricourt: "to bring out the panchronic conditions governing the evolution from nasal consonants to oral consonants". Transfers of nasality between a syllable-initial consonant cluster and the following vowel can go in both directions: from the consonantal onset to the following vowel in Tai-Kadai, Austroasiatic, Sino-Tibetan, Niger-Congo (Kwa) and Indo-European (Celtic), and from the vowel to the preceding consonant in Siouan.

Importantly, an examination of the conditions on these changes brings out an asymmetry. In most cases, transfers of nasality take place from a consonantal onset to a following vowel; instances of a regular change in the opposite direction all come from languages where there is one of the following restrictions on nasal sounds: (i) nasal consonants are nonphonemic (contextually predictable), or (ii) the opposition between nasal and oral vowels is neutralized after nasal consonants (in favour of nasal vowels).

In languages without phonemic nasal consonants, /CṼ/ > [NṼ] is a ubiquitous synchronic rule. In languages that have phonemic nasal consonants but no oppositions between /NṼ/ and /NV/, the change from /CṼ/ to /NṼ/, despite resulting in the neutralization of some oppositions, is not unheard of; however, it is usually restricted to specific morphological contexts. Finally, no case of spreading of distinctive nasality from a vowel to a preceding consonant has been found so far in languages that have an opposition between /NṼ/ and /NV/ [START_REF] Michaud | Historical transfer of nasality between consonantal onset and vowel: from C to V or from V to C?[END_REF].

  On the basis of the examples of languages of Indochina, it would seem that what matters in the 1. [The original mistakenly indicates 'anticipation' instead of 'carry-over'.]

  understand that nasal consonants([m] and[n]) in the Kan-on type of pronunciation have changed to[b] and [d], respectively, in Japanese. How do you account then for the presence in contemporary Japanese of [ŋ] (the so-called "Japanese syllabic 'n' ") which may seem due to a mutation different from that for [m] and [n]? And would you consider[ŋ] as in Kanon as a nasal consonant or a sort of nasal vowel? Answer by A.-G. Haudricourt:No! The change from mto mb-, and from nto nd-, took place in China; thereafter, mbchanged to b-, and ndto d-, when these sounds passed into Japanese. The finals -m and -n were pronounced in a vocalic way, because Japanese is a language that only allows open syllables.2 The confusion of the two finals -m and -n results in a nasal vowel that has the vowel quality of the vowel that precedes: /kan/ is actually [kaa], /hon/ is [hoo], etc.

Table 1 .

 1 Examples illustrating consonant correspondences between the Numea dialects of Goro (conservative dialect) and Unia (advanced dialect).

	gloss	Goro	Unia	path of evolution
	earthworm currency, money wet	mbie mie mi ̃ẽ	pie mbie m i e	mb>p m>mb m V ̃>mV

Table 4 .

 4 are observed. Correspondences between Ponca and Santee[START_REF] Boas | Siouan (Dakota). Handbook of American Indian Languages 1[END_REF].The same process took place in the Chinese dialects of the Min group, such

	gloss	Ponca	Santee
	with the foot by cutting	na-ma-	n a-b a-

as the dialects of Amoy [Xiamen] 厦 门 (Fujian 福 建 province) and Chaozhou 潮州 (Guangdong 广东 province). The Amoy dialect contrasts oral vowels and nasal vowels, but m-, nand ŋare only found before nasal vowels, and b-, land gbefore oral vowels, e.g. geʔni ̃ũ 'moon', meñi ̃ 'next year', and bin nã lit 'tomorrow' (Linguistics Centre 1964). The existence of the doublet mẽ -bin ['bright, clear', 明 ], corresponding to miŋ in the Beijing dialect, reveals what has happened. The nasal vowels belong to an old vernacular layer: the nasal vowels in that language variety originated in the elision of final nasal consonants. Then a learned pronunciation was introduced, with an oral vowel followed by a final nasal consonant [i.e. a more archaic state of affairs than in the vernacular]. It is in these learned words that [the conditions of change were met, as in the examples in Table


Mickael Rouvier

Georges Linarès

Benjamin Lecouteux

Query-driven strategy for on-the-fly term spotting in spontaneous speech

Spoken utterance retrieval was largely studied in the last decades, with the purpose of indexing large audio databases or of detecting keywords in continuous speech streams. While the indexing of closed corpora can be performed via a batch process, on-line spotting systems have to synchronously detect the targeted spoken utterances. We propose a two-level architecture for on-the-fly term spotting. The first level performs a fast detection of the speech segments that probably contain the targeted utterance. The second level refines the detection on the selected segments, by using a speech recognizer based on a query-driven decoding algorithm. Experiments are conducted on both broadcast and spontaneous speech corpora. We investigate the impact of the spontaneity level on system performance. Results show that our method remains effective even if the recognition rates are significantly degraded by disfluencies.

Introduction

Term detection has been extensively studied in the last decades in the two different contexts of spoken term detection (STD): large speech databases and keyword spotting in continuous speech streams. The first topic recently faced a growing interest, stemming from the critical need of content-based structuring of audio-visual collections. Since the STD task relies on the indexing of the whole speech database, word spotting systems perform a sequential parsing of the speech stream with the purpose of detecting the targeted word sequence. Here, we focus on on-the-fly term spotting, where the detection must be synchronously notified, at the moment where it occurs in the speech stream. This task refers to a usage scenario where early detection is critical, such as supervision and automation of operator-assisted calls [START_REF] Wilpon | Automatic recognition of keywords in unconstrained speech using hidden Markov models[END_REF][START_REF] Wohlford | The enhancement of wordspotting techniques[END_REF].

For all these detection tasks, performances reported in the literature are quite good on clean conditions, especially on broadcast news data that were largely used for speech processing system benchmarking [START_REF] Fiscus | Results of the 2006 spoken term detection evaluation[END_REF][START_REF] Garofolo | The TREC spoken document retrieval track: A success story[END_REF]. In more difficult conditions, such as noisy or spontaneous speech, performances are dramatically degraded by recognition errors [START_REF] Pinto | Fast Approximate Spoken Term Detection from Sequence of Phonemes[END_REF][START_REF] Yu | Vocabulary-independent indexing of spontaneous speech[END_REF][START_REF] Saraclar | Lattice-based search for spoken utterance retrieval[END_REF].

Efficiency and scalability issues are generally considered as critical in detection tasks, due to the size of speech databases or, in the spotting case, due to the need of as soon as possible (ASAP) detection. Some aspects of this problem are commonly encountered in both spotting and large vocabulary continuous speech recognition (LVCSR) contexts, such as fast likelihood computation [START_REF] Bocchieri | Subspace distribution clustering for continuous observation density hidden markov models[END_REF][START_REF] Ortmanns | Fast likelihood computation methods for continuous mixture densities in large vocabulary speech recognition[END_REF] or fast acoustic matching [START_REF] Knill | Fast implementation methods for Viterbi-based word-spotting[END_REF][START_REF] Cardillo | Phonetic searching vs. LVCSR: How to find what you really want in audio archives[END_REF]. On the STD task, the search algorithm operates on data that were indexed by an off-line process. On-the-fly term spotting adds new problems due to on-line processing: the entire speech database is not available for indexing, and the full processing chain, from the signal to the final decision, must be performed as fast as possible in order to limit the delay between the speech utterance and the notification of detection.

Even if using only a real-time recognition system could be envisaged, this approach has two major drawbacks: first, strict pruning schemes have to be used to reach real time, impacting dramatically on the word error rate (WER), especially in adverse acoustic conditions; secondly, automatic speech recognition (ASR) usually relies on closed dictionaries, and some specific modeling strategies have to be used for out-of-vocabulary words (OOV) handling. This lexical coverage problem is a key issue in term detection, the system effectiveness being highly sensitive to it: OOV are frequently meaningful words and may probably be queried by the user. A solution is to map the terms in a sub-lexical representation allowing for the search of terms without using any recognition lexicon [START_REF] Manos | A study on out-of-vocabulary word modelling for a segmentbased keyword spotting system[END_REF].

Generally, subword-level decoding consists in a fast acoustic matching between the signal and the phonetic or syllabic transcription of the term [START_REF] Rose | Definition of subword acoustic units for wordspotting[END_REF][START_REF] Lau | Providing sublexical constraints for word spotting within the angie framework[END_REF]. Various developments of this idea were evaluated in the past, with the purpose of being able to detect OOV and of improving system robustness [START_REF] Manos | A study on out-of-vocabulary word modelling for a segmentbased keyword spotting system[END_REF]. In [START_REF] Pinto | Fast Approximate Spoken Term Detection from Sequence of Phonemes[END_REF], the phonetic search integrates the phoneme confusion matrix in order to limit the impact of recognition errors. Other authors combine complementary acoustic scoring methods, for example Gaussian mixture models (GMM) and multilayer perceptrons (MLP)-based estimators [START_REF] Pinto | Exploiting phoneme similarities in hybrid HMM-ANN keyword spotting[END_REF][START_REF] Bourlard | Optimizing recognition and rejection performance in wordspotting systems[END_REF]. In [START_REF] Yu | Vocabulary-independent indexing of spontaneous speech[END_REF], the authors propose, in the context of the STD task, to estimate the scores of the OOV by combining the posterior probabilities of their phonetic substrings. Therefore, many of the fast wordspotters are based on off-line phonetic matching. They generally use two models representing respectively the targeted word (or term) and the "garbage", the latter aiming to "absorb" all non-targeted utterances [START_REF] Bourlard | Optimizing recognition and rejection performance in wordspotting systems[END_REF][START_REF] Manos | A segment-based wordspotter using phonetic filler models[END_REF][START_REF] Junkawitsch | A new keyword spotting algorithm with pre-calculated optimal thresholds[END_REF]. These models are built from Hidden Markov Models (HMM) representing sub-lexical units, typically phonemes or triphones.

Phonetics-based approaches allow for a high speed spotting and OOV detection, but the system's performance suffers from a lack of linguistic information that help distinguish targeted terms from phonetically close utterances [START_REF] Szoke | Comparison of keyword spotting approaches for informal continuous speech[END_REF], especially on short phonetic sequences [START_REF] Cardillo | Phonetic searching vs. LVCSR: How to find what you really want in audio archives[END_REF]. Therefore, many authors proposed hybrid approaches that combine phonetic search and ASR-based detection in off-line detection systems, in both spotting and STD contexts [START_REF] Szoke | Hybrid word-subword decoding for spoken term detection[END_REF][START_REF] Logan | Approaches to reduce the effects of OOV queries on indexed spoken audio[END_REF][START_REF] Akbacak | Openvocabulary spoken term detection using graphone-based hybrid recognition systems[END_REF][START_REF] Mamou | Vocabulary independent spoken term detection[END_REF].

In this paper, we investigate the use of such a hybrid approach in the specific context of on-the-fly term spotting. We propose a two-level architecture in which the first level performs a phonetic filtering of the speech streams, while the second level involves an open-vocabulary LVCSR system. These two cascaded components are optimized in order to sequentially maximize the recall at the first level, and precision at the second.

At the first level, fast-matching is viewed as a filtering task that aims to accept or reject segments, according to the probability of the targeted terms being inside. Starting from this idea, we present a general scheme in which the term pronunciation graph is mapped into a graph of phonetic filters. The resulting graph is then pruned in order to minimize its complexity, while maximizing its detection capacity.

At the second level, speech segments that passed the first filtering step are processed by an ASR-based term spotter with the purpose of refining the term detection. We propose to improve the detection rate by integrating the query (i.e. the searched word sequence). This integration is based on the driven decoding algorithm (DDA) that was previously proposed in [START_REF] Lecouteux | Imperfect transcript driven speech recognition[END_REF]).

The rest of the paper is organized as follows: Section 2 presents the global architecture of our term spotter. Section 3 describes the first level, that aims to identify the speech segments in which the query probably is. We first present a GMM-based approach to acoustic filtering, and we extend the method to neuromimetic filtering. In Section 4, we present the second level, where a querydriven decoding strategy is used for refining the term spotting. In Section 5, we present the experimental framework; results on clean and spontaneous speech are reported and discussed in Section 6. Finally, we conclude the paper and we propose some perspectives.

Principle and System Architecture

Starting from a text query composed of a short sequence of words, the term spotting system is supposed to scan a speech stream and to synchronously notify any occurrence of the targeted word sequence.

The global processing chain consists of two stages. In the first stage, the spotter is configured according to the query. This query-dependent adaptation concerns the two main components of the system that are: (i) the phonetic spotter, and (ii) the query-driven ASR system. Obviously, no information about the speech stream is available at this moment. Then, the detection system is ready to perform the synchronous scanning of the speech stream.

The approach that we propose consists in building, at the first stage, a querydependent detection system that has to be as accurate as possible, while being able to perform on-the-fly detection. We use a two-level architecture, where the first level performs a fast, but poorly accurate detection, the detection hypotheses being validated by a more costly detection process at the second level.

Written queries are first phonetically transcribed by using a pronunciation lexicon and a rule-based phonetizer, which produces the word-sequence pronunciation graph. Starting from this phonetic representation, an acoustic filter is built, that is composed from a graph of phonetic filters. Phonetic filters may be based on GMM or MLP. In the following, the full filtering graph is named acoustic filter, while phonetic filters operate at the node level.

At this point, our goal is to maximize the accuracy and the computational efficiency under the constraint of maximal recall rates.

Each speech segment selected by the first level is passed to the second level, as shown in Figure 1. The second level consists of an ASR system based on the driven-decoding algorithm. At this stage, ASR-based processing aims to refine the detection, focusing on precision improvement.

Acoustic filtering

Query encoding

The first step consists in transcribing the written query to phonetic strings. All the pronunciation variants of in-vocabulary terms are extracted from a dictionary that has been manually checked, since the OOV are automatically transcribed by using a rule-based phonetization system. Then, all these phonetic transcriptions are compiled in a graph of phonemes where each path represents a pronunciation variant. Classical approaches use such representations for spotting words by aligning the graph and the signal in a sliding window, the global path probability being used for taking the final detection decision. This approach is sub-optimal in terms of CPU-resource consumption: evaluation of the full path probability is generally useless as the intermediate scores could be sufficient to prune the lowprobability paths. We implement such an as soon as possible cutoff by plugging, to each graph node, a phonetic filter that will be able to stop or to continue the path evaluation process. This filtering process is described in detail in the next two sections, where GMM-based and neuromimetic filters are presented.

Considering this strategy of ASAP cutoff, it is clear that the most discriminative parts of the graph should be evaluated first, with the purpose of reducing the CPU-time while preserving the spotting accuracy. Therefore, the graph may be reduced according to both the complexity and the discriminative capacity of its sub-graphs. We propose a graph reduction algorithm that is fully described in Section 3.4.

GMM-based phonetic filters

GMM-based filters use the acoustic models involved in the ASR system. Each filter f i is associated to an emitting state S i extracted from the HMM set of the ASR system. The phonetic graph is developed according to the HMM topologies, each phoneme-dependent node being splitted into a sequence of n state-dependent nodes.

The resulting state-dependent filters should be able to stop the graph exploration when observation X t is out of the model domain. This is achieved by specifying, for each filter, a lower limit c i for the normalized likelihood ll(X t |S i ):

ll(X t |S i ) = P (X t |S i ) P (X t |U BM ) (1)
where X t is a speech frame of 39 coefficients, composed of 12 PLP coefficients, energy and their first and second order derivatives. The Universal Background Model (U BM ) is a generic model that represents the speech signal, independently of the phonetic units. Here, U BM is a GM M of 64 Gaussian components, estimated by using the Expectation-Maximization procedure on the training corpus.

The filter-dependent cutoff thresholds c i are estimated on the training set, by computing the upper bound of c i values, under the constraint ll(X t |S i ) > c i , ∀X t ∈ Ω i , where Ω i is the subset of the training corpus emitted by state S i .

When the final node of the graph is reached (i.e. all phonetic filters were passed), a last selection rule is applied at the segment level. This rule relies on the full path probabilities of the targeted terms, normalized by the segment duration. We search first, in the training corpus, the lowest probability of the targeted terms. We use this lower bound C as a rejection threshold. Therefore, all accepted speech sequences X = {X t } satisfy the constraint:

P (X|S) > C (2)
where S = {S i } is the state sequence corresponding to the phonetic string, and C is the query-dependent threshold.

Neuromimetic phonetic filters

Discriminative methods for word spotting have been recently investigated in [START_REF] Keshet | Discriminative keyword spotting[END_REF][START_REF] Ezzat | Discriminative word-spotting using ordered spectrotemporal patch features[END_REF][START_REF] Benayed | Confidence measure for keyword spotting using support vector machines[END_REF]. This approach is motivated by the fact that spotting should be stated as a classification task (in rejected/accepted hypotheses), rather than a probability estimation task. The goal of acoustic filtering is to reject non-relevant segments. Considering that discriminative approaches should be more efficient for segment filtering, we propose the use of multi-layer neural networks as phonetic filters.

MLP-based filtering integrates the general scheme that was used with GMMbased filters, GMM-based phonetic filters being simply substituted for MLP classifiers as probability estimators.

We use one MLP classifier for each of the emitting states S i that compose an initial context-independent HMM set. We follow the modeling method proposed in [START_REF] Ellis | Size matters: an empirical study of neural network training for large vocabulary continuous speech recognition[END_REF][START_REF] Zhu | Using MLP features in SRI's conversational speech recognition system[END_REF]. MLP filters operate at the frame level. The input vectors are composed of 351 coefficients, resulting from the concatenation of 9 frames of 39 coefficients each. The latter are classical 12 PLP coefficients, energy, and the first and second order derivatives of these 13 components. The hidden layer is composed of 1024 cells. MLPs are trained on a large corpus by using the classical back-propagation learning rule. This training step relies on a state-level segmentation that is performed by using the ASR system and its HMM-based acoustic models.

Each classifier has one output layer that is supposed to provide an estimation of the probability P (X t |S i ) of the frame X t , given the state S i . MLP-based phonetic filters are then integrated in the filtering graphs in a similar way to GMM-based filters: a cutoff threshold c i is associated to each of these neural nets, allowing for the rejection of the detection hypothesis when the output score is low. The c i values are computed on the training corpus, by estimating the lowest output value obtained by the positive training examples emitted by the S i states. A segment-level threshold C is used for rejecting the detection hypothesis when the full path probability P (X|ph) is lower than C. This full path probability is estimated by a Viterbi alignment based on neural probability estimators, and normalized according to the size of the considered path.

Finally, the filtering strategy is strictly similar to the one used in the GMM case. MLPs are used as probability estimators, and integrated as phonetic filters with respect to the global filtering scheme initially designed for the GMM-based filtering.

Smart phonetic queries

The basic idea of this mechanism is that some parts of the phonetic query may have a discriminative capacity significantly better than others, for different reasons; first, the less frequent a phoneme-sequence is, the more specific to the targeted term this sequence is. Secondly, according to the phonetic filter performances, the use of partial queries may provide a better complexity/accuracy trade-off. For example, the search for "olympic games" could be reduced to the phonetic pattern "ympic g...", with a significant computational gain and without any significant negative impact on accuracy. It is important to note that the recall rates are not influenced by the query reduction, an utterance spotted by the full phonetic string being necessarily spotted by any of its substrings.

A similar issue has been addressed in a different context by [START_REF] Yu | Vocabulary-independent indexing of spontaneous speech[END_REF][START_REF] Allauzen | General indexation of weighted automata application to spoken utterance retrieval[END_REF]. The authors proposed to handle OOV queries in an audio search task. They approximated term frequency by backing-off to the frequency of the phonetic substrings of the targeted terms. Our idea is to find the optimal substring in terms of both accuracy and complexity, with the purpose of maximizing the former, while minimizing the latter.

At this point, the question is how to find the best subgraph. The first step is to define an objective function F ob (f ) that quantifies the complexity/accuracy trade-off for a given filter f associated to a multi-word query W .

For simplicity, we first linearize the graph by merging competing models into a common phonetic filter. The resulting filter f = {f i } i=0,n is composed of the cascade of the n phonetic filters f i , corresponding to a phonetic sequence ph and to the associated state sequence S i . The relevance of f is estimated via the objective function F ob (f ) that combines a computational cost term cpx(f ) with an accuracy index acc(f ).

We use a complexity index cpx() that relies on an estimate of the number of frames that may be submitted to each phonetic filter f k . The probability of reaching f i depends on the probability of passing all the previous filters f k,i>k≥0 in the cascade of filters. In order to estimate the probability of passing a filter f i , we associate, to each of them, a random variable D i (X t ) that indicates whether a frame passed the filter, or not. Therefore, D i is set to 1 when the inequality ll(X t |S i ) > c i holds, and D i is set to 0 otherwise. The prior probability of passing f i is denoted by P (D i = 1). Prior probabilities are estimated by counting the number of frames that pass the filter on the training corpus, normalized by the total number of frames.

The prior probability of reaching the phonetic filter i is the product of the prior probabilities P (D k = 1), k < i of passing the previous filters f k .

Finally, the computational cost of the cascade f of filters is estimated by summing over all prior probabilities of reaching the filters from the cascade:

cpx(f ) = g * (1 + n k=0 k i=0 P (D i = 1)) (3)
were g is a constant computational cost factor that will be set to 1 in our experiments.

The accuracy of the filter f = {f i , f i-...f 0 } can be defined as the prior probability that f performs a correct detection. This value depends on two elements. First, the smart phonetic query may match an incorrect word utterance even if the two phonetic strings are identical. For example, the search for "Olympic games" by using the very short sub-query "pic" will probably return many wrong, but acoustically close, words such "picture". Secondly, the phonetic filters may fail, by accepting false utterances.

The first element may be evaluated by estimating, in the training corpus, the probability of the targeted term W when the phonetic sequence ph is encountered. This value is computed as follows:

P (W |ph) = |W | |ph| (4)
where |W | is the number of utterances of the term W in the training corpus, and |ph| is the number of utterances of the phonetic sequence ph in the same corpus.

In a similar way, the phonetic filter accuracy P (S i |D i = 1) represents the prior probability that the filter f i perform a correct detection. This value is estimated on the training corpus, by counting the number of frames that passed the filter, while actually being emitted by the state S i .

Finally, the global accuracy of the filter f is estimated according to the accuracy of each of its phonetic filters f i and to the accuracy of the phonetic sequence ph = {S i }:

acc(f ) = P (W |ph) * n i=0 P (S i |D i = 1) (5)
The objective function is defined as:

F ob (f ) = acc(f ) -γ * cpx(f ) (6)
where γ is a fudge factor empirically determined. This function is used for sub-queries ranking, the selected smart phonetic query being the one that maximizes F ob :

f sq = arg max k F ob (f k ) (7)
For each query W , the sub-query selection is achieved by an exhaustive evaluation of all parts of the cascade of filters f . Then, the initial full filter f is substituted for the sub-query f sq , and this reduced filter is used for the acoustic filtering achieved at the first level in our system.

This technique of best phonetic substring search is used for both GMM-based and MLP-based system. Nevertheless, the F ob function relies on the accuracy of the phonetic filters f i that are dependent on the frame-level probability estimators. Therefore, the smart phonetic query selection process is performed independently for the GMM and MLP based filtering methods.

Query-driven decoding

The goal of this step is to refine the detection achieved at the first level. Speech segments that passed the filtering process are submitted to the ASR system for a full decoding pass. In order to be sure that the speech segment contains the full targeted speech utterance even if only a part of the phonetic string is spotted (due to smart queries), we enlarge the segment before and after the spotted area. In our experiments, we used an offset of 0.5 second from the segment borders.

Spotting by using ASR systems is known to be focused on accuracy, since the prior probability of having the targeted terms in a transcription is low. On the other hand, transcription errors may introduce mistakes and lead to misses of correct utterances, especially on large queries: the longer the searched term is, the higher the probability of encountering an erroneous word is. In order to limit this risk, the prior probability of the query is slightly boosted by the driven decoding algorithm (DDA) [START_REF] Lecouteux | Imperfect transcript driven speech recognition[END_REF].

This algorithm aims to align a priori transcripts by using a speech recognition engine. The algorithm proceeds in two steps. First, the provided transcripts h p and the current hypothesis h c are synchronized by using an alignment algorithm by minimization of the editing distance between the two word strings h p and h c .

Once the hypothesis is aligned with the transcript, the algorithm estimates the matching transcript-to-hypothesis score (denoted α). This score is based on the number of words in the short-term history, which are correctly aligned with the transcript: only three values are used, corresponding respectively to a full alignment of the current trigram, a full alignment of the current bi-gram and an alignment of one word only. Values of α are empirically determined, by testing various configurations on a development corpus. Then, trigram probabilities are modified by using the following re-scoring rule:

P (w i |w i-1 , w i-2 ) = P 1-α (w i |w i-1 , w i-2 ) (8)
where P (w i |w i-1 , w i-2 ) is the updated trigram probability of a word w i knowing the history w i-1 , w i-2 , and P (w i |w i-1 , w i-2 ) is the initial probability of the trigram.

Here, we used DDA as a post-processor operating on a segment previously identified as a good candidate by the acoustic filter. The targeted terms are used as a priori transcripts, leading to a slight boosting of the linguistic scores of the hypotheses that match the query.

At this step, OOV probabilities are interpolated by backing off to unknown word probabilities. Unknown word probabilities are estimated classically: we tag as unk all the words in the training set that are out of the recognition vocabulary. Then, unk is viewed as a word and its linguistic probabilities are classically estimated.

Then, a trigram probability of an OOV word w oov can be decomposed according to the conditional probability of the unknown word and the probability of w oov , given unk:

P (w oov |w -1 , w -2 ) = P (w oov |unk) * P (unk|w -1 , w -2 ) (9)
Here, we use a priorly fixed value for P (w oov |unk). In the following experiments, this probability is set to 10 -4 .

Experimental framework

The LIA broadcast news system

The experiments reported in this paper are carried out by using the LIA broadcast news (BN) system, which was involved in the ESTER evaluation campaign [START_REF] Linarès | A scalable system for embedded large vocabulary continuous speech recognition[END_REF]. This system relies on an A * decoder with HMM-based context-dependent acoustic models and trigram language models. HMMs are classical three-state left-right models; state tying is achieved by using decision trees. Acoustic vectors are composed of 12 PLP coefficients, the energy, and first and second order derivatives of these 13 parameters. Two configurations are involved in the experiments, according to their decoding speed expressed as a real time factor, i.e. the time required by the system to decode one hour of speech signal. We used the real-time (noted 1xRT) and the three times real-time (noted 3xRT) systems in the experiments. The 1xRT system uses acoustic models that have only 24 Gaussian components per state and a strict pruning scheme, whereas the 3xRT system relies on 64 Gaussians per state models.

The EPAC and ESTER corpora

ESTER is a large corpus developed in the framework of the ESTER-2005 evaluation campaign. It is composed of 80 hours of French broadcast news. We use these materials as a training set, for both GMM and MLP estimates. Tests are conducted on the EPAC corpus, which is provided by the EPAC project [START_REF] Dufour | Spontaneous speech characterization and detection in large audio databases[END_REF]. This project aims to investigate methods for spontaneous speech recognition and understanding. With this purpose, about 11 hours of spontaneous speech were extracted from the non-transcribed ESTER database and manually labeled according to their degree of spontaneity: degree 1 stands for read speech, and degree 10 stands for highly disfluent speech. Here, we consider two classes: medium, corresponding respectively to degrees 1 to 4, and high, corresponding to degrees 5 and above.

In the sequel, the EPAC corpus is used only for testing. Acoustic filtering and smart querying are calibrated on the ESTER training materials. 270 test queries composed the test set, including 130 in-vocabulary (IV) queries, 70 OOV and 70 hybrid queries, the latter including both known words and OOV. The query size is 1 to 4 words long, hybrid queries being composed of at least 2 words. The baseline performance of the ASR system in the 1xRT configuration is 40.3% WER, corresponding to WERs of 33.2% and 47.2% on medium and highly spontaneous subsets, respectively. In the 3xRT configuration, these rates decrease to 31.1% and 43.5%.

Results

Phonetic filtering evaluation

The acoustic filtering is evaluated in various configurations. The baseline system consists of a classical phonetic matching, which uses a Viterbi alignment between the phonetic graph and the signal window. The acoustic models are context-independent HMMs trained on ESTER data. We first study the impact of the ASAP pruning technique (A-GMM). Then, smart querying is added to the previous filtering system (A+SR-GMM). Finally, we evaluate MLP-based filtering, with ASAP pruning and smart querying (A+SR+MLP). In Table 1 we show the results on the EPAC spontaneous speech database in terms of recall rates, real-time factor (RT-factor) and filtering rates, the latter being the cumulated duration of the selected speech segments, normalized by the whole duration of the speech stream. Results show that the ASAP pruning technique allows for a drastic reduction of the number of accepted speech segments. Smart querying does not impact significantly on the filtering rates, but provides a strong CPU-time saving, the filtering time being reduced by a factor of two. Comparisons between GMM and MLP filters demonstrate the efficiency of a discriminative approach in such a filtering task. As expected, MLP performs a much more selective filtering of the speech segments (from 37% to 23%), at similar recall rates.

Evaluation of the query-driven decoding strategy

Here, the performance of the full system is evaluated. We report baseline results obtained with the LIA real-time ASR system (ASR-1xRT). In order to have a glimpse on the performance of the system without strong constraints on the decoding time, results for the 3xRT system are also reported. For these two systems, no query-dependent mechanisms are used, the search of terms being directly performed on the outputs of the ASR system.

Then, we estimate the detection rates by using DDA only, without acoustic filtering (DDA-1xRT). Considering the filtering of speech streams, only 37% of the whole speech duration has to be processed by the recognition system (and 23% for MLP).

Methods based on both acoustic filtering and driven decoding are evaluated by using a more accurate ASR system. We take advantage of the filtering by using a 3xRT configuration, the full process satisfying the real-time constraint.

Performances obtained with the full filtering method based on GMM (GMM+ DDA-3xRT) and on MLP (MLP+DDA-3xRT) are reported in Table 2 in terms of the F-measure, which is computed as the harmonic mean of the recall and the precision: The results show that DDA provides significant improvements in all cases: by using the real-time DDA algorithm, the F-measure is similar to the one obtained with the best ASR-only 3xRT configuration, which is clearly out of the speed requirement for on-the-fly processing. The full two-level system benefits from both acoustic filtering and query-driven decoding; the absolute F-measure gain is of 20%, compared to the ASR-1xRT on IV queries. Compared to the DDA-1xRT system that handles OOV queries, the combination of acoustic filtering and query-driven decoding provides an absolute F-measure gain of about 8%. The boosting of linguistic probabilities seems to be really efficient for ASR-based spotting: on multi-word queries, the prior probability of making a mistake in the whole sequence is high. By integrating the query itself in the recognition process, we provide additional information that tends to limit the errors on the targeted utterance.

F = 2 * precision * recall precision + recall (10)
The last point is that all DDA-based systems perform better on OOV and hybrid queries. OOV are relatively long, and the size of the phonetic sequences clearly helps in the identification process. Moreover, the use of penalized unknown word probabilities tends to increase the real probability of the targeted terms, which would be very low on OOV, the recognition lexicon being built by collecting the most frequent words from the training corpus.

Detection performance according to the spontaneity level

The following experiments investigate the impact of the spontaneity level on the detection rates. We use the classification in medium and high spontaneity level, by relying on our two-level STD system. The results for the ASR system with query-driven decoding (DDA-1xRT) are reported in Table 3. As expected, the performances are significantly affected The acoustic filtering clearly provides a gain in all the conditions, but the more interesting point is that it seems to be highly robust to spontaneous speech: the results reported in Table 4 show that GMM-based filtering leads to similar results on medium and high spontaneity levels in terms of F-measure, the degradation of the precision rate being compensated by the improvement of the recall rate. The MLP-based system (see Table 6.3) outperforms the GMM-based system on medium spontaneity degrees (from 0.78 to 0.83), but the F-measure is affected by speech spontaneity. For highly spontaneous speech, GMM and MLP -based approaches perform similarly.

CONCLUSIONS AND PERSPECTIVES

We presented a two-level architecture for on-the-fly term spotting, where the full process is query-driven. The first level relies on an optimized representation of the query as a cascade of phonetic filters. The second level performs a query-driven decoding on speech segments that passed the first-level filter. We evaluated the performance of this technique on spontaneous speech. Results demonstrated that ASAP pruning combined with sub-query search improves significantly the phonetic matching efficiency, in all test conditions. Moreover, query-driven decoding provides a significant improvement compared to unconstrained decoding. The performances according to the level of spontaneity show that the proposed methods are be more robust to disfluencies than ASR-only ones, with respect to the real-time constraint.

Globally, experiments demonstrate the interest of integrating query-dependent information in the detection process, especially with ASR-based spotting. At the acoustic level, this allows for a fast matching that benefits from the particularities of the query phonetic sequence. At the linguistic level, boosting the n-gram probabilities of the word sequence improves significantly the performance of ASR-based spotting systems.

Since the proposed architecture is designed for on-the-fly term spotting, some of the techniques herein could be used in the spoken term detection task as well. We now plan to develop our proposal in this way.

Figure 1 :

 1 Figure 1: A two-level architecture for on-the-fly term detection. The query is encoded as an acoustic filter that extracts relevant areas from the speech stream. Speech segments that passed the filter are processed by a query-driven speech recognizer.

Figure 2 :

 2 Figure2: From the requested term to the smart query. The written query is transcribed into a pronunciation graph. The best subgraph, which maximizes its accuracy while minimizing its CPU cost, is extracted to build the smart query.

Table 1 :

 1 Acoustic filtering performed by a simple Viterbi alignment on context-free HMMs (Baseline), with GMM-filtering and ASAP pruning (A-GMM), with ASAP and smart querying coupled with GMM-based filters (A-SR-GMM) and MLP-based filters (A-SR-MLP). Performances are reported in terms of recall, filtering rates and CPU time consumption. Tests are conducted on the EPAC spontaneous speech corpus.

		Baseline A-GMM A-SR-GMM A-SR-MLP
	Recall	0.99	0.97	0.97	0.97
	Filt. rate	0.65	0.33	0.37	0.23
	RT-fact.	0.1	0.05	0.03	0.05

Table 2 :

 2 The F-measure on the EPAC spontaneous speech test set, for the ASR-only approach in real-time (ASR-1xRT) and 3xRT configurations (3xRT), for the ASR with driven decoding algorithm and without acoustic filtering (DDA-1xRT), on the full system including acoustic filtering and driven decoding, with GMM-based filters (DDA-AF-GMM) and MLP-based filters (DDA-AF-MLP).

	System	IV	OOV Hybrid Total
	ASR-3xRT	0.66	x	x	x
	ASR-1xRT	0.56	x	x	x
	DDA-1xRT	0.65	0.79	0.75	0.72
	DDA-AF-GMM 0.78	0.86	0.76	0.77
	DDA-AF-MLP	0.76	0.89	0.80	0.80

Table 3 :

 3 Detection rates for the real-time ASR system with query-driven decoding DDA-1xRT, according to the level of spontaneity. Tests are conducted on the 11-hour EPAC test corpus, by using 270 queries composed of 1 to 4 words (70 OOV queries, 70 hybrid and 130 IV queries) .

	Spontaneity Level Recall Precision F-measure
	Medium	0.63	0.97	0.76
	High	0.62	0.65	0.63

Table 4 :

 4 Detection rates of the two-level system with GMM-based acoustic filtering and querydriven decoding DDA-AF-GMM, according to the level of spontaneity. Tests are conducted on the 11-hour EPAC test corpus, by using 270 queries composed of 1 to 4 words (70 OOV queries, 70 hybrid and 130 IV queries).

	Spontaneity Level Recall Precision F-measure
	Medium	0.65	0.97	0.78
	High	0.74	0.81	0.77

Table 5 :

 5 Detection rates of the two-level system with Neuromimetic acoustic filtering and query-driven decoding DDA-AF-MLP, according to the level of spontaneity. Tests are conducted on the 11-hour EPAC test corpus, by using 270 queries composed of 1 to 4 words (70 OOV queries, 70 hybrid and 130 IV queries). , the F-measure decreasing from 0.76 to 0.63; the recall rates remain stable, but the precision rate decreases by about 0.32 in absolute value.

	Spontaneity Level Recall Precision F-measure
	Medium	0.73	0.97	0.83
	High	0.73	0.83	0.78
	by disfluent speech