Effects of errorless learning on the acquisition of velopharyngeal movement control

Session 1pSC - Speech Communication: Cross-Linguistic Studies of Speech Sound Learning of the Languages of Hong Kong (Poster Session)The implicit motor learning literature suggests a benefit for learning if errors are minimized during practice. This study investigated whether the same principle holds for learning velopharyngeal movement control. Normal speaking participants learned to produce hypernasal speech in either an errorless learning condition (in which the possibility for errors was limited) or an errorful learning condition (in which the possibility for errors was not limited). Nasality level of the participants’ speech was measured by nasometer and reflected by nasalance scores (in %). Errorless learners practiced producing hypernasal speech with a threshold nasalance score of 10% at the beginning, which gradually increased to a threshold of 50% at the end. The same set of threshold targets were presented to errorful learners but in a reversed order. Errors were defined by the proportion of speech with a nasalance score below the threshold. The results showed that, relative to errorful learners, errorless learners displayed fewer errors (50.7% vs. 17.7%) and a higher mean nasalance score (31.3% vs. 46.7%) during the acquisition phase. Furthermore, errorless learners outperformed errorful learners in both retention and novel transfer tests. Acknowledgment: Supported by The University of Hong Kong Strategic Research Theme for Sciences of Learning © 2012 Acoustical Society of Americapublished_or_final_versio

Speech technologies for the audiovisual and multimedia interaction environments

361 p

Robust learning of acoustic representations from diverse speech data

Automatic speech recognition is increasingly applied to new domains. A key challenge is to robustly learn, update and maintain representations to cope with transient acoustic conditions. A typical example is broadcast media, for which speakers and environments may change rapidly, and available supervision may be poor. The concern of this thesis is to build and investigate methods for acoustic modelling that are robust to the characteristics and transient conditions as embodied by such media. The first contribution of the thesis is a technique to make use of inaccurate transcriptions as supervision for acoustic model training. There is an abundance of audio with approximate labels, but training methods can be sensitive to label errors, and their use is therefore not trivial. State-of-the-art semi-supervised training makes effective use of a lattice of supervision, inherently encoding uncertainty in the labels to avoid overfitting to poor supervision, but does not make use of the transcriptions. Existing approaches that do aim to make use of the transcriptions typically employ an algorithm to filter or combine the transcriptions with the recognition output from a seed model, but the final result does not encode uncertainty. We propose a method to combine the lattice output from a biased recognition pass with the transcripts, crucially preserving uncertainty in the lattice where appropriate. This substantially reduces the word error rate on a broadcast task. The second contribution is a method to factorise representations for speakers and environments so that they may be combined in novel combinations. In realistic scenarios, the speaker or environment transform at test time might be unknown, or there may be insufficient data to learn a joint transform. We show that in such cases, factorised, or independent, representations are required to avoid deteriorating performance. Using i-vectors, we factorise speaker or environment information using multi-condition training with neural networks. Specifically, we extract bottleneck features from networks trained to classify either speakers or environments. The resulting factorised representations prove beneficial when one factor is missing at test time, or when all factors are seen, but not in the desired combination. The third contribution is an investigation of model adaptation in a longitudinal setting. In this scenario, we repeatedly adapt a model to new data, with the constraint that previous data becomes unavailable. We first demonstrate the effect of such a constraint, and show that using a cyclical learning rate may help. We then observe that these successive models lend themselves well to ensembling. Finally, we show that the impact of this constraint in an active learning setting may be detrimental to performance, and suggest to combine active learning with semi-supervised training to avoid biasing the model. The fourth contribution is a method to adapt low-level features in a parameter-efficient and interpretable manner. We propose to adapt the filters in a neural feature extractor, known as SincNet. In contrast to traditional techniques that warp the filterbank frequencies in standard feature extraction, adapting SincNet parameters is more flexible and more readily optimised, whilst maintaining interpretability. On a task adapting from adult to child speech, we show that this layer is well suited for adaptation and is very effective with respect to the small number of adapted parameters

Adaptation of speech recognition systems to selected real-world deployment conditions

Tato habilitační práce se zabývá problematikou adaptace systémů rozpoznávání řeči na vybrané reálné podmínky nasazení. Je koncipována jako sborník celkem dvanácti článků, které se touto problematikou zabývají. Jde o publikace, jejichž jsem hlavním autorem nebo spoluatorem, a které vznikly v rámci několika navazujících výzkumných projektů. Na řešení těchto projektů jsem se podílel jak v roli člena výzkumného týmu, tak i v roli řešitele nebo spoluřešitele. Publikace zařazené do tohoto sborníku lze rozdělit podle tématu do tří hlavních skupin. Jejich společným jmenovatelem je snaha přizpůsobit daný rozpoznávací systém novým podmínkám či konkrétnímu faktoru, který významným způsobem ovlivňuje jeho funkci či přesnost. První skupina článků se zabývá úlohou neřízené adaptace na mluvčího, kdy systém přizpůsobuje svoje parametry specifickým hlasovým charakteristikám dané mluvící osoby. Druhá část práce se pak věnuje problematice identifikace neřečových událostí na vstupu do systému a související úloze rozpoznávání řeči s hlukem (a zejména hudbou) na pozadí. Konečně třetí část práce se zabývá přístupy, které umožňují přepis audio signálu obsahujícího promluvy ve více než v jednom jazyce. Jde o metody adaptace existujícího rozpoznávacího systému na nový jazyk a metody identifikace jazyka z audio signálu. Obě zmíněné identifikační úlohy jsou přitom vyšetřovány zejména v náročném a méně probádaném režimu zpracování po jednotlivých rámcích vstupního signálu, který je jako jediný vhodný pro on-line nasazení, např. pro streamovaná data.This habilitation thesis deals with adaptation of automatic speech recognition (ASR) systems to selected real-world deployment conditions. It is presented in the form of a collection of twelve articles dealing with this task; I am the main author or a co-author of these articles. They were published during my work on several consecutive research projects. I have participated in the solution of them as a member of the research team as well as the investigator or a co-investigator. These articles can be divided into three main groups according to their topics. They have in common the effort to adapt a particular ASR system to a specific factor or deployment condition that affects its function or accuracy. The first group of articles is focused on an unsupervised speaker adaptation task, where the ASR system adapts its parameters to the specific voice characteristics of one particular speaker. The second part deals with a) methods allowing the system to identify non-speech events on the input, and b) the related task of recognition of speech with non-speech events, particularly music, in the background. Finally, the third part is devoted to the methods that allow the transcription of an audio signal containing multilingual utterances. It includes a) approaches for adapting the existing recognition system to a new language and b) methods for identification of the language from the audio signal. The two mentioned identification tasks are in particular investigated under the demanding and less explored frame-wise scenario, which is the only one suitable for processing of on-line data streams

Hizketa-ezagutzan oinarritutako estrategiak, euskarazko online OBHI (Ordenagailu Bidezko Hizkuntza Ikaskuntza) sistemetarako

211 p. (eng) 217 p. (eusk.)Tesi honetan, euskarazko hizketa-ezagutze automatikoaren bi inplementazio aztertzen dira, Ordenagailu Bidezko Hizkuntza Ikaskuntza (OBHI) sistemetarako: Ordenagailu Bidezko Ebakera Lanketa (OBEL) eta Ahozko Gramatika Praktika (AGP). OBEL sistema klasikoan, erabiltzaileari esaldi bat irakurrarazten zaio, eta fonema bakoitzerako puntuazio bat jasotzen du bueltan. AGPn, Hitzez Hitzeko Esaldi Egiaztapena (HHEE) teknika proposatu dugu, ariketak ebatzi ahala egiaztatzen dituen sistema. Bi sistemon oinarrian, esakuntza egiaztatzeko teknikak daude, Goodness of Pronunciation (GOP) puntuazioa, adibididez.Sistema horiek inplementatzeko, eredu akustikoak entrenatu behar dira, eta, horretarako, Basque Speecon-like datu-basea erabili dugu, euskararako publikoki erabilgarri dagoen datu-base bakarra. Eredu akustiko onak lortzearren, datu-basean egokitzapenak egin behar izan dira hiztegi alternatibadun bat sortuz, eta fasekako entrenamendua ere probatu da. % 12.21eko PER (fonemen errore-tasa) lortu da hala.Lehendabiziko sistema laborategiko baldintzetan testatu da, eta emaitza lehiakorrak lortu dira.Hala ere, tesi honetako OBEL eta AGP sistemen helburua da bezero/zerbitzari motako arkitektura batean ezartzea, ikasleek edonondik atzi dezaten. Hori ahalbidetzeko, HTML5eko zehaztapenak erabili dira audioa zerbitzarira grabatu ahala bidaltzeko, eta, gainera, onlineko batezbesteko- eta bariantza-normalizazio cepstraleko (CMVN, Cepstral Mean and Variance Normalisation) teknika berri bat proposatu da erabiltzaileek grabatutako audio-seinaleen kanal desberdintasunen eragina txikiagotzeko. Teknika hori tesi honetan aurkeztutako metodo batean oinarriturik dago: normalizazio anitzeko puntuatzea (MNS, Multi Normalization Scoring), eta onlineko ahots-aktibitatearen detektagailu (VAD, Voice Activity Detector) berri bat ere proposatu da metodo horretan oinarriturik. Azkenik, parametro desberdinak ebaluatu dira neurona-sareak erabiliz, eta ondorioztatu da GOP puntuazioa dela eraginkorrena