55 research outputs found
Low Resource Efficient Speech Retrieval
Speech retrieval refers to the task of retrieving the information, which is useful or relevant to a user query, from speech collection. This thesis aims to examine ways in which speech retrieval can be improved in terms of requiring low resources - without extensively annotated corpora on which automated processing systems are typically built - and achieving high computational efficiency.
This work is focused on two speech retrieval technologies, spoken keyword retrieval and spoken document classification. Firstly, keyword retrieval - also referred to as keyword search (KWS) or spoken term detection - is defined as the task of retrieving the occurrences of a keyword specified by the user in text form, from speech collections. We make advances in an open vocabulary KWS platform using context-dependent Point Process Model (PPM). We further accomplish a PPM-based lattice generation framework, which improves KWS performance and enables automatic speech recognition (ASR) decoding.
Secondly, the massive volumes of speech data motivate the effort to organize and search speech collections through spoken document classification. In classifying real-world unstructured speech into predefined classes, the wildly collected speech recordings can be extremely long, of varying length, and contain multiple class label shifts at variable locations in the audio. For this reason each spoken document is often first split into sequential segments, and then each segment is independently classified. We present a general purpose method for classifying spoken segments, using a cascade of language independent acoustic modeling, foreign-language to English translation lexicons, and English-language classification. Next, instead of classifying each segment independently, we demonstrate that exploring the contextual dependencies across sequential segments can provide large classification performance improvements. Lastly, we remove the need of any orthographic lexicon and instead exploit alternative unsupervised approaches to decoding speech in terms of automatically discovered word-like or phoneme-like units. We show that the spoken segment representations based on such lexical or phonetic discovery can achieve competitive classification performance as compared to those based on a domain-mismatched ASR or a universal phone set ASR
LOW RESOURCE HIGH ACCURACY KEYWORD SPOTTING
Keyword spotting (KWS) is a task to automatically detect keywords of interest in continuous speech, which has been an active research topic for over 40 years. Recently there is a rising demand for KWS techniques in resource constrained conditions. For example, as for the year of 2016, USC Shoah Foundation covers audio-visual testimonies from survivors and other witnesses of the Holocaust in 63 countries and 39 languages, and providing search capability for those testimonies requires substantial KWS technologies in low language resource conditions, as for most languages, resources for developing KWS systems are not as rich as that for English.
Despite the fact that KWS has been in the literature for a long time, KWS techniques in resource constrained conditions have not been researched extensively. In this dissertation, we improve KWS performance in two low resource conditions: low language resource condition where language specific data is inadequate, and low computation resource condition where KWS runs on computation constrained devices.
For low language resource KWS, we focus on applications for speech data mining, where large vocabulary continuous speech recognition (LVCSR)-based KWS techniques are widely used. Keyword spotting for those applications are also known as keyword search (KWS) or spoken term detection (STD). A key issue for this type of KWS technique is the out-of-vocabulary (OOV) keyword problem. LVCSR-based KWS can only search for words that are defined in the LVCSR's lexicon, which is typically very small in a low language resource condition. To alleviate the OOV keyword problem, we propose a technique named "proxy keyword search" that enables us to search for OOV keywords with regular LVCSR-based KWS systems. We also develop a technique that expands LVCSR's lexicon automatically by adding hallucinated words, which increases keyword coverage and therefore improves KWS performance. Finally we explore the possibility of building LVCSR-based KWS systems with limited lexicon, or even without an expert pronunciation lexicon.
For low computation resource KWS, we focus on wake-word applications, which usually run on computation constrained devices such as mobile phones or tablets. We first develop a deep neural network (DNN)-based keyword spotter, which is lightweight and accurate enough that we are able to run it on devices continuously. This keyword spotter typically requires a pre-defined keyword, such as "Okay Google". We then propose a long short-term memory (LSTM)-based feature extractor for query-by-example KWS, which enables the users to define their own keywords
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
Language Identification and Morphosyntactic Tagging: The Second VarDial Evaluation Campaign
We present the results and the findings of the Second VarDial Evaluation Campaign on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects. The campaign was organized as part of the fifth edition of the VarDial workshop, collocated with COLING’2018. This year, the campaign included five shared tasks, including two task re-runs – Arabic Dialect Identification (ADI) and German Dialect Identification (GDI) –, and three new tasks – Morphosyntactic Tagging of Tweets (MTT), Discriminating between Dutch and Flemish in Subtitles (DFS), and Indo-Aryan Language Identification (ILI). A total of 24 teams submitted runs across the five shared tasks, and contributed 22 system description papers, which were included in the VarDial workshop proceedings and are referred to in this report.Non peer reviewe
Morphologically motivated word classes for very large vocabulary speech recognition of Finnish and Estonian
We study class-based n-gram and neural network language models for very large vocabulary speech recognition of two morphologically rich languages: Finnish and Estonian. Due to morphological processes such as derivation, inflection and compounding, the models need to be trained with vocabulary sizes of several millions of word types. Class-based language modelling is in this case a powerful approach to alleviate the data sparsity and reduce the computational load. For a very large vocabulary, bigram statistics may not be an optimal way to derive the classes. We thus study utilizing the output of a morphological analyzer to achieve efficient word classes. We show that efficient classes can be learned by refining the morphological classes to smaller equivalence classes using merging, splitting and exchange procedures with suitable constraints. This type of classification can improve the results, particularly when language model training data is not very large. We also extend the previous analyses by rescoring the hypotheses obtained from a very large vocabulary recognizer using class-based neural network language models. We show that despite the fixed vocabulary, carefully constructed classes for word-based language models can in some cases result in lower error rates than subword-based unlimited vocabulary language models.We study class-based n-gram and neural network language models for very large vocabulary speech recognition of two morphologically rich languages: Finnish and Estonian. Due to morphological processes such as derivation, inflection and compounding, the models need to be trained with vocabulary sizes of several millions of word types. Class-based language modelling is in this case a powerful approach to alleviate the data sparsity and reduce the computational load. For a very large vocabulary, bigram statistics may not be an optimal way to derive the classes. We thus study utilizing the output of a morphological analyzer to achieve efficient word classes. We show that efficient classes can be learned by refining the morphological classes to smaller equivalence classes using merging, splitting and exchange procedures with suitable constraints. This type of classification can improve the results, particularly when language model training data is not very large. We also extend the previous analyses by rescoring the hypotheses obtained from a very large vocabulary recognizer using class-based neural network language models. We show that despite the fixed vocabulary, carefully constructed classes for word-based language models can in some cases result in lower error rates than subword-based unlimited vocabulary language models.Peer reviewe
Multilingual training of deep neural networks
We investigate multilingual modeling in the context of a deep neural network (DNN) – hidden Markov model (HMM) hy-brid, where the DNN outputs are used as the HMM state like-lihoods. By viewing neural networks as a cascade of fea-ture extractors followed by a logistic regression classifier, we hypothesise that the hidden layers, which act as feature ex-tractors, will be transferable between languages. As a corol-lary, we propose that training the hidden layers on multiple languages makes them more suitable for such cross-lingual transfer. We experimentally confirm these hypotheses on the GlobalPhone corpus using seven languages from three dif-ferent language families: Germanic, Romance, and Slavic. The experiments demonstrate substantial improvements over a monolingual DNN-HMM hybrid baseline, and hint at av-enues of further exploration. Index Terms — Speech recognition, deep learning, neural networks, multilingual modelin
Dynamic Acoustic Unit Augmentation With BPE-Dropout for Low-Resource End-to-End Speech Recognition
With the rapid development of speech assistants, adapting server-intended
automatic speech recognition (ASR) solutions to a direct device has become
crucial. Researchers and industry prefer to use end-to-end ASR systems for
on-device speech recognition tasks. This is because end-to-end systems can be
made resource-efficient while maintaining a higher quality compared to hybrid
systems. However, building end-to-end models requires a significant amount of
speech data. Another challenging task associated with speech assistants is
personalization, which mainly lies in handling out-of-vocabulary (OOV) words.
In this work, we consider building an effective end-to-end ASR system in
low-resource setups with a high OOV rate, embodied in Babel Turkish and Babel
Georgian tasks. To address the aforementioned problems, we propose a method of
dynamic acoustic unit augmentation based on the BPE-dropout technique. It
non-deterministically tokenizes utterances to extend the token's contexts and
to regularize their distribution for the model's recognition of unseen words.
It also reduces the need for optimal subword vocabulary size search. The
technique provides a steady improvement in regular and personalized
(OOV-oriented) speech recognition tasks (at least 6% relative WER and 25%
relative F-score) at no additional computational cost. Owing to the use of
BPE-dropout, our monolingual Turkish Conformer established a competitive result
with 22.2% character error rate (CER) and 38.9% word error rate (WER), which is
close to the best published multilingual system.Comment: 16 pages, 7 figure
Linguistic knowledge-based vocabularies for Neural Machine Translation
This article has been published in a revised form in Natural Language Engineering https://doi.org/10.1017/S1351324920000364. This version is free to view and download for private research and study only. Not for re-distribution, re-sale or use in derivative works. © Cambridge University PressNeural Networks applied to Machine Translation need a finite vocabulary to express textual information as a sequence of discrete tokens. The currently dominant subword vocabularies exploit statistically-discovered common parts of words to achieve the flexibility of character-based vocabularies without delegating the whole learning of word formation to the neural network. However, they trade this for the inability to apply word-level token associations, which limits their use in semantically-rich areas and prevents some transfer learning approaches e.g. cross-lingual pretrained embeddings, and reduces their interpretability. In this work, we propose new hybrid linguistically-grounded vocabulary definition strategies that keep both the advantages of subword vocabularies and the word-level associations, enabling neural networks to profit from the derived benefits. We test the proposed approaches in both morphologically rich and poor languages, showing that, for the former, the quality in the translation of out-of-domain texts is improved with respect to a strong subword baseline.This work is partially supported by Lucy Software / United Language Group (ULG) and the Catalan Agency for Management of University and Research Grants (AGAUR) through an Industrial PhD Grant. This work is also supported in part by the Spanish Ministerio de Economa y Competitividad, the European Regional Development Fund and the Agencia Estatal de Investigacin, through the postdoctoral senior grant Ramn y Cajal, contract TEC2015-69266-P (MINECO/FEDER,EU) and contract PCIN-2017-079 (AEI/MINECO).Peer ReviewedPostprint (author's final draft
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