Text Injection for Capitalization and Turn-Taking Prediction in Speech Models

Text injection for automatic speech recognition (ASR), wherein unpaired text-only data is used to supplement paired audio-text data, has shown promising improvements for word error rate. This study examines the use of text injection for auxiliary tasks, which are the non-ASR tasks often performed by an E2E model. In this work, we use joint end-to-end and internal language model training (JEIT) as our text injection algorithm to train an ASR model which performs two auxiliary tasks. The first is capitalization, which is a de-normalization task. The second is turn-taking prediction, which attempts to identify whether a user has completed their conversation turn in a digital assistant interaction. We show results demonstrating that our text injection method boosts capitalization performance for long-tail data, and improves turn-taking detection recall

Multimodal Fusion of Polynomial Classifiers for Automatic Person Recognition

With the prevalence of the information age, privacy and personalization are forefront in today\u27s society. As such, biometrics are viewed as essential components of current and evolving technological systems. Consumers demand unobtrusive and noninvasive approaches. In our previous work, we have demonstrated a speaker verification system that meets these criteria. However, there are additional constraints for fielded systems. The required recognition transactions are often performed in adverse environments and across diverse populations, necessitating robust solutions. There are two significant problem areas in current generation speaker verification systems. The first is the difficulty in acquiring clean audio signals (in all environments) without encumbering the user with a head-mounted close-talking microphone. Second, unimodal biometric systems do not work with a significant percentage of the population. To combat these issues, multimodal techniques are being investigated to improve system robustness to environmental conditions, as well as improve overall accuracy across the population. We propose a multimodal approach that builds on our current state-of-the-art speaker verification technology. In order to maintain the transparent nature of the speech interface, we focus on optical sensing technology to provide the additional modality–giving us an audio-visual person recognition system. For the audio domain, we use our existing speaker verification system. For the visual domain, we focus on lip motion. This is chosen, rather than static face or iris recognition, because it provides dynamic information about the individual. In addition, the lip dynamics can aid speech recognition to provide liveness testing. The visual processing method makes use of both color and edge information, combined within a Markov random field (MRF) framework, to localize the lips. Geometric features are extracted and input to a polynomial classifier for the person recognition process. A late integration approach, based on a probabilistic model, is employed to combine the two modalities. The system is tested on the XM2VTS database combined with AWGN (in the audio domain) over a range of signal-to-noise ratios

Alignment Knowledge Distillation for Online Streaming Attention-based Speech Recognition

This article describes an efficient training method for online streaming attention-based encoder-decoder (AED) automatic speech recognition (ASR) systems. AED models have achieved competitive performance in offline scenarios by jointly optimizing all components. They have recently been extended to an online streaming framework via models such as monotonie chunkwise attention (MoChA). However, the elaborate attention calculation process is not robust against long-form speech utterances. Moreover, the sequence-level training objective and time-restricted streaming encoder cause a nonnegligible delay in token emission during inference. To address these problems, we propose CTC synchronous training (CTC-ST), in which CTC alignments are leveraged as a reference for token boundaries to enable a MoChA model to learn optimal monotonie input-output alignments. We formulate a purely end-to-end training objective to synchronize the boundaries of MoChA to those of CTC. The CTC model shares an encoder with the MoChA model to enhance the encoder representation. Moreover, the proposed method provides alignment information learned in the CTC branch to the attention-based decoder. Therefore, CTC-ST can be regarded as self-distillation of alignment knowledge from CTC to MoChA. Experimental evaluations on a variety of benchmark datasets show that the proposed method significantly reduces recognition errors and emission latency simultaneously. The robustness to long-form and noisy speech is also demonstrated. We compare CTC-ST with several methods that distill alignment knowledge from a hybrid ASR system and show that the CTC-ST can achieve a comparable tradeoff of accuracy and latency without relying on external alignment information

Speech Activity and Speaker Change Point Detection for Online Streams

Disertační práce je věnována dvěma si blízkým řečovým úlohám a následně jejich použití v online prostředí. Konkrétně se jedná o úlohy detekce řeči a detekce změny mluvčího. Ty jsou často nedílnou součástí systémů pro zpracování řeči (např. pro diarizaci mluvčích nebo rozpoznávání řeči), kde slouží pro předzpracování akustického signálu. Obě úlohy jsou v literatuře velmi aktivním tématem, ale většina existujících prací je směřována primárně na offline využití. Nicméně právě online nasazení je nezbytné pro některé řečové aplikace, které musí fungovat v reálném čase (např. monitorovací systémy).Úvodní část disertační práce je tvořena třemi kapitolami. V té první jsou vysvětleny základní pojmy a následně je nastíněno využití obou úloh. Druhá kapitola je věnována současnému poznání a je doplněna o přehled existujících nástrojů. Poslední kapitola se skládá z motivace a z praktického použití zmíněných úloh v monitorovacích systémech. V závěru úvodní části jsou stanoveny cíle práce.Následující dvě kapitoly jsou věnovány teoretickým základům obou úloh. Představují vybrané přístupy, které jsou buď relevantní pro disertační práci (porovnání výsledků), nebo jsou zaměřené na použití v online prostředí.V další kapitole je předložen finální přístup pro detekci řeči. Postupný návrh tohoto přístupu, společně s experimentálním vyhodnocením, je zde detailně rozebrán. Přístup dosahuje nejlepších výsledků na korpusu QUT-NOISE-TIMIT v podmínkách s nízkým a středním zašuměním. Přístup je také začleněn do monitorovacího systému, kde doplňuje svojí funkcionalitou rozpoznávač řeči.Následující kapitola detailně představuje finální přístup pro detekci změny mluvčího. Ten byl navržen v rámci několika po sobě jdoucích experimentů, které tato kapitola také přibližuje. Výsledky získané na databázi COST278 se blíží výsledkům, kterých dosáhl referenční offline systém, ale předložený přístup jich docílil v online módu a to s nízkou latencí.Výstupy disertační práce jsou shrnuty v závěrečné kapitole.The main focus of this thesis lies on two closely interrelated tasks, speech activity detection and speaker change point detection, and their applications in online processing. These tasks commonly play a crucial role of speech preprocessors utilized in speech-processing applications, such as automatic speech recognition or speaker diarization. While their use in offline systems is extensively covered in literature, the number of published works focusing on online use is limited.This is unfortunate, as many speech-processing applications (e.g., monitoring systems) are required to be run in real time.The thesis begins with a three-chapter opening part, where the first introductory chapter explains the basic concepts and outlines the practical use of both tasks. It is followed by a chapter, which reviews the current state of the art and lists the existing toolkits. That part is concluded by a chapter explaining the motivation behind this work and the practical use in monitoring systems; ultimately, this chapter sets the main goals of this thesis.The next two chapters cover the theoretical background of both tasks. They present selected approaches relevant to this work (e.g., used for result comparisons) or focused on online processing.The following chapter proposes the final speech activity detection approach for online use. Within this chapter, a detailed description of the development of this approach is available as well as its thorough experimental evaluation. This approach yields state-of-the-art results under low- and medium-noise conditions on the standardized QUT-NOISE-TIMIT corpus. It is also integrated into a monitoring system, where it supplements a speech recognition system.The final speaker change point detection approach is proposed in the following chapter. It was designed in a series of consecutive experiments, which are extensively detailed in this chapter. An experimental evaluation of this approach on the COST278 database shows the performance of approaching the offline reference system while operating in online mode with low latency.Finally, the last chapter summarizes all the results of this thesis

Robust speech recognition with spectrogram factorisation

Communication by speech is intrinsic for humans. Since the breakthrough of mobile devices and wireless communication, digital transmission of speech has become ubiquitous. Similarly distribution and storage of audio and video data has increased rapidly. However, despite being technically capable to record and process audio signals, only a fraction of digital systems and services are actually able to work with spoken input, that is, to operate on the lexical content of speech. One persistent obstacle for practical deployment of automatic speech recognition systems is inadequate robustness against noise and other interferences, which regularly corrupt signals recorded in real-world environments. Speech and diverse noises are both complex signals, which are not trivially separable. Despite decades of research and a multitude of different approaches, the problem has not been solved to a sufficient extent. Especially the mathematically ill-posed problem of separating multiple sources from a single-channel input requires advanced models and algorithms to be solvable. One promising path is using a composite model of long-context atoms to represent a mixture of non-stationary sources based on their spectro-temporal behaviour. Algorithms derived from the family of non-negative matrix factorisations have been applied to such problems to separate and recognise individual sources like speech. This thesis describes a set of tools developed for non-negative modelling of audio spectrograms, especially involving speech and real-world noise sources. An overview is provided to the complete framework starting from model and feature definitions, advancing to factorisation algorithms, and finally describing different routes for separation, enhancement, and recognition tasks. Current issues and their potential solutions are discussed both theoretically and from a practical point of view. The included publications describe factorisation-based recognition systems, which have been evaluated on publicly available speech corpora in order to determine the efficiency of various separation and recognition algorithms. Several variants and system combinations that have been proposed in literature are also discussed. The work covers a broad span of factorisation-based system components, which together aim at providing a practically viable solution to robust processing and recognition of speech in everyday situations

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

Speech processing using digital MEMS microphones

The last few years have seen the start of a unique change in microphones for consumer devices such as smartphones or tablets. Almost all analogue capacitive microphones are being replaced by digital silicon microphones or MEMS microphones. MEMS microphones perform differently to conventional analogue microphones. Their greatest disadvantage is significantly increased self-noise or decreased SNR, while their most significant benefits are ease of design and manufacturing and improved sensitivity matching. This thesis presents research on speech processing, comparing conventional analogue microphones with the newly available digital MEMS microphones. Specifically, voice activity detection, speaker diarisation (who spoke when), speech separation and speech recognition are looked at in detail. In order to carry out this research different microphone arrays were built using digital MEMS microphones and corpora were recorded to test existing algorithms and devise new ones. Some corpora that were created for the purpose of this research will be released to the public in 2013. It was found that the most commonly used VAD algorithm in current state-of-theart diarisation systems is not the best-performing one, i.e. MLP-based voice activity detection consistently outperforms the more frequently used GMM-HMM-based VAD schemes. In addition, an algorithm was derived that can determine the number of active speakers in a meeting recording given audio data from a microphone array of known geometry, leading to improved diarisation results. Finally, speech separation experiments were carried out using different post-filtering algorithms, matching or exceeding current state-of-the art results. The performance of the algorithms and methods presented in this thesis was verified by comparing their output using speech recognition tools and simple MLLR adaptation and the results are presented as word error rates, an easily comprehensible scale. To summarise, using speech recognition and speech separation experiments, this thesis demonstrates that the significantly reduced SNR of the MEMS microphone can be compensated for with well established adaptation techniques such as MLLR. MEMS microphones do not affect voice activity detection and speaker diarisation performance

Harvesting and summarizing user-generated content for advanced speech-based human-computer interaction

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 155-164).There have been many assistant applications on mobile devices, which could help people obtain rich Web content such as user-generated data (e.g., reviews, posts, blogs, and tweets). However, online communities and social networks are expanding rapidly and it is impossible for people to browse and digest all the information via simple search interface. To help users obtain information more efficiently, both the interface for data access and the information representation need to be improved. An intuitive and personalized interface, such as a dialogue system, could be an ideal assistant, which engages a user in a continuous dialogue to garner the user's interest and capture the user's intent, and assists the user via speech-navigated interactions. In addition, there is a great need for a type of application that can harvest data from the Web, summarize the information in a concise manner, and present it in an aggregated yet natural way such as direct human dialogue. This thesis, therefore, aims to conduct research on a universal framework for developing speech-based interface that can aggregate user-generated Web content and present the summarized information via speech-based human-computer interaction. To accomplish this goal, several challenges must be met. Firstly, how to interpret users' intention from their spoken input correctly? Secondly, how to interpret the semantics and sentiment of user-generated data and aggregate them into structured yet concise summaries? Lastly, how to develop a dialogue modeling mechanism to handle discourse and present the highlighted information via natural language? This thesis explores plausible approaches to tackle these challenges. We will explore a lexicon modeling approach for semantic tagging to improve spoken language understanding and query interpretation. We will investigate a parse-and-paraphrase paradigm and a sentiment scoring mechanism for information extraction from unstructured user-generated data. We will also explore sentiment-involved dialogue modeling and corpus-based language generation approaches for dialogue and discourse. Multilingual prototype systems in multiple domains have been implemented for demonstration.by Jingjing Liu.Ph.D

Keskusteluavustimen kehittäminen kuulovammaisia varten automaattista puheentunnistusta käyttäen

Understanding and participating in conversations has been reported as one of the biggest challenges hearing impaired people face in their daily lives. These communication problems have been shown to have wide-ranging negative consequences, affecting their quality of life and the opportunities available to them in education and employment. A conversational assistance application was investigated to alleviate these problems. The application uses automatic speech recognition technology to provide real-time speech-to-text transcriptions to the user, with the goal of helping deaf and hard of hearing persons in conversational situations. To validate the method and investigate its usefulness, a prototype application was developed for testing purposes using open-source software. A user test was designed and performed with test participants representing the target user group. The results indicate that the Conversation Assistant method is valid, meaning it can help the hearing impaired to follow and participate in conversational situations. Speech recognition accuracy, especially in noisy environments, was identified as the primary target for further development for increased usefulness of the application. Conversely, recognition speed was deemed to be sufficient and already surpass the transcription speed of human transcribers.Keskustelupuheen ymmärtäminen ja keskusteluihin osallistuminen on raportoitu yhdeksi suurimmista haasteista, joita kuulovammaiset kohtaavat jokapäiväisessä elämässään. Näillä viestintäongelmilla on osoitettu olevan laaja-alaisia negatiivisia vaikutuksia, jotka heijastuvat elämänlaatuun ja heikentävät kuulovammaisten yhdenvertaisia osallistumismahdollisuuksia opiskeluun ja työelämään. Työssä kehitettiin ja arvioitiin apusovellusta keskustelupuheen ymmärtämisen ja keskusteluihin osallistumisen helpottamiseksi. Sovellus käyttää automaattista puheentunnistusta reaaliaikaiseen puheen tekstittämiseen kuuroja ja huonokuuloisia varten. Menetelmän toimivuuden vahvistamiseksi ja sen hyödyllisyyden tutkimiseksi siitä kehitettiin prototyyppisovellus käyttäjätestausta varten avointa lähdekoodia hyödyntäen. Testaamista varten suunniteltiin ja toteutettiin käyttäjäkoe sovelluksen kohderyhmää edustavilla koekäyttäjillä. Saadut tulokset viittaavat siihen, että työssä esitetty Keskusteluavustin on toimiva ja hyödyllinen apuväline huonokuuloisille ja kuuroille. Puheentunnistustarkkuus erityisesti meluisissa olosuhteissa osoittautui ensisijaiseksi kehityskohteeksi apusovelluksen hyödyllisyyden lisäämiseksi. Puheentunnistuksen nopeus arvioitiin puolestaan jo riittävän nopeaksi, ylittäen selkeästi kirjoitustulkkien kirjoitusnopeuden