Speech Dereverberation Based on Integrated Deep and Ensemble Learning Algorithm

Reverberation, which is generally caused by sound reflections from walls, ceilings, and floors, can result in severe performance degradation of acoustic applications. Due to a complicated combination of attenuation and time-delay effects, the reverberation property is difficult to characterize, and it remains a challenging task to effectively retrieve the anechoic speech signals from reverberation ones. In the present study, we proposed a novel integrated deep and ensemble learning algorithm (IDEA) for speech dereverberation. The IDEA consists of offline and online phases. In the offline phase, we train multiple dereverberation models, each aiming to precisely dereverb speech signals in a particular acoustic environment; then a unified fusion function is estimated that aims to integrate the information of multiple dereverberation models. In the online phase, an input utterance is first processed by each of the dereverberation models. The outputs of all models are integrated accordingly to generate the final anechoic signal. We evaluated the IDEA on designed acoustic environments, including both matched and mismatched conditions of the training and testing data. Experimental results confirm that the proposed IDEA outperforms single deep-neural-network-based dereverberation model with the same model architecture and training data

Reverberation: models, estimation and application

The use of reverberation models is required in many applications such as acoustic measurements, speech dereverberation and robust automatic speech recognition. The aim of this thesis is to investigate different models and propose a perceptually-relevant reverberation model with suitable parameter estimation techniques for different applications. Reverberation can be modelled in both the time and frequency domain. The model parameters give direct information of both physical and perceptual characteristics. These characteristics create a multidimensional parameter space of reverberation, which can be to a large extent captured by a time-frequency domain model. In this thesis, the relationship between physical and perceptual model parameters will be discussed. In the first application, an intrusive technique is proposed to measure the reverberation or reverberance, perception of reverberation and the colouration. The room decay rate parameter is of particular interest. In practical applications, a blind estimate of the decay rate of acoustic energy in a room is required. A statistical model for the distribution of the decay rate of the reverberant signal named the eagleMax distribution is proposed. The eagleMax distribution describes the reverberant speech decay rates as a random variable that is the maximum of the room decay rates and anechoic speech decay rates. Three methods were developed to estimate the mean room decay rate from the eagleMax distributions alone. The estimated room decay rates form a reverberation model that will be discussed in the context of room acoustic measurements, speech dereverberation and robust automatic speech recognition individually

Environmentally robust ASR front-end for deep neural network acoustic models

This paper examines the individual and combined impacts of various front-end approaches on the performance of deep neural network (DNN) based speech recognition systems in distant talking situations, where acoustic environmental distortion degrades the recognition performance. Training of a DNN-based acoustic model consists of generation of state alignments followed by learning the network parameters. This paper first shows that the network parameters are more sensitive to the speech quality than the alignments and thus this stage requires improvement. Then, various front-end robustness approaches to addressing this problem are categorised based on functionality. The degree to which each class of approaches impacts the performance of DNN-based acoustic models is examined experimentally. Based on the results, a front-end processing pipeline is proposed for efficiently combining different classes of approaches. Using this front-end, the combined effects of different classes of approaches are further evaluated in a single distant microphone-based meeting transcription task with both speaker independent (SI) and speaker adaptive training (SAT) set-ups. By combining multiple speech enhancement results, multiple types of features, and feature transformation, the front-end shows relative performance gains of 7.24% and 9.83% in the SI and SAT scenarios, respectively, over competitive DNN-based systems using log mel-filter bank features.This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.csl.2014.11.00

Spatial features of reverberant speech: estimation and application to recognition and diarization

Distant talking scenarios, such as hands-free calling or teleconference meetings, are essential for natural and comfortable human-machine interaction and they are being increasingly used in multiple contexts. The acquired speech signal in such scenarios is reverberant and affected by additive noise. This signal distortion degrades the performance of speech recognition and diarization systems creating troublesome human-machine interactions.This thesis proposes a method to non-intrusively estimate room acoustic parameters, paying special attention to a room acoustic parameter highly correlated with speech recognition degradation: clarity index. In addition, a method to provide information regarding the estimation accuracy is proposed. An analysis of the phoneme recognition performance for multiple reverberant environments is presented, from which a confusability metric for each phoneme is derived. This confusability metric is then employed to improve reverberant speech recognition performance. Additionally, room acoustic parameters can as well be used in speech recognition to provide robustness against reverberation. A method to exploit clarity index estimates in order to perform reverberant speech recognition is introduced. Finally, room acoustic parameters can also be used to diarize reverberant speech. A room acoustic parameter is proposed to be used as an additional source of information for single-channel diarization purposes in reverberant environments. In multi-channel environments, the time delay of arrival is a feature commonly used to diarize the input speech, however the computation of this feature is affected by reverberation. A method is presented to model the time delay of arrival in a robust manner so that speaker diarization is more accurately performed.Open Acces

Deep Learning for Distant Speech Recognition

Deep learning is an emerging technology that is considered one of the most promising directions for reaching higher levels of artificial intelligence. Among the other achievements, building computers that understand speech represents a crucial leap towards intelligent machines. Despite the great efforts of the past decades, however, a natural and robust human-machine speech interaction still appears to be out of reach, especially when users interact with a distant microphone in noisy and reverberant environments. The latter disturbances severely hamper the intelligibility of a speech signal, making Distant Speech Recognition (DSR) one of the major open challenges in the field. This thesis addresses the latter scenario and proposes some novel techniques, architectures, and algorithms to improve the robustness of distant-talking acoustic models. We first elaborate on methodologies for realistic data contamination, with a particular emphasis on DNN training with simulated data. We then investigate on approaches for better exploiting speech contexts, proposing some original methodologies for both feed-forward and recurrent neural networks. Lastly, inspired by the idea that cooperation across different DNNs could be the key for counteracting the harmful effects of noise and reverberation, we propose a novel deep learning paradigm called network of deep neural networks. The analysis of the original concepts were based on extensive experimental validations conducted on both real and simulated data, considering different corpora, microphone configurations, environments, noisy conditions, and ASR tasks.Comment: PhD Thesis Unitn, 201

A Bayesian Network View on Acoustic Model-Based Techniques for Robust Speech Recognition

This article provides a unifying Bayesian network view on various approaches for acoustic model adaptation, missing feature, and uncertainty decoding that are well-known in the literature of robust automatic speech recognition. The representatives of these classes can often be deduced from a Bayesian network that extends the conventional hidden Markov models used in speech recognition. These extensions, in turn, can in many cases be motivated from an underlying observation model that relates clean and distorted feature vectors. By converting the observation models into a Bayesian network representation, we formulate the corresponding compensation rules leading to a unified view on known derivations as well as to new formulations for certain approaches. The generic Bayesian perspective provided in this contribution thus highlights structural differences and similarities between the analyzed approaches

Channel selection and reverberation-robust automatic speech recognition

If speech is acquired by a close-talking microphone in a controlled and noise-free environment, current state-of-the-art recognition systems often show an acceptable error rate. The use of close-talking microphones, however, may be too restrictive in many applications. Alternatively, distant-talking microphones, often placed several meters far from the speaker, may be used. Such setup is less intrusive, since the speaker does not have to wear any microphone, but the Automatic Speech Recognition (ASR) performance is strongly affected by noise and reverberation. The thesis is focused on ASR applications in a room environment, where reverberation is the dominant source of distortion, and considers both single- and multi-microphone setups. If speech is recorded in parallel by several microphones arbitrarily located in the room, the degree of distortion may vary from one channel to another. The difference among the signal quality of each recording may be even more evident if those microphones have different characteristics: some are hanging on the walls, others standing on the table, or others build in the personal communication devices of the people present in the room. In a scenario like that, the ASR system may benefit strongly if the signal with the highest quality is used for recognition. To find such signal, what is commonly referred as Channel Selection (CS), several techniques have been proposed, which are discussed in detail in this thesis. In fact, CS aims to rank the signals according to their quality from the ASR perspective. To create such ranking, a measure that either estimates the intrinsic quality of a given signal, or how well it fits the acoustic models of the recognition system is needed. In this thesis we provide an overview of the CS measures presented in the literature so far, and compare them experimentally. Several new techniques are introduced, that surpass the former techniques in terms of recognition accuracy and/or computational efficiency. A combination of different CS measures is also proposed to further increase the recognition accuracy, or to reduce the computational load without any significant performance loss. Besides, we show that CS may be used together with other robust ASR techniques, and that the recognition improvements are cumulative up to some extent. An online real-time version of the channel selection method based on the variance of the speech sub-band envelopes, which was developed in this thesis, was designed and implemented in a smart room environment. When evaluated in experiments with real distant-talking microphone recordings and with moving speakers, a significant recognition performance improvement was observed. Another contribution of this thesis, that does not require multiple microphones, was developed in cooperation with the colleagues from the chair of Multimedia Communications and Signal Processing at the University of Erlangen-Nuremberg, Erlangen, Germany. It deals with the problem of feature extraction within REMOS (REverberation MOdeling for Speech recognition), which is a generic framework for robust distant-talking speech recognition. In this framework, the use of conventional methods to obtain decorrelated feature vector coefficients, like the discrete cosine transform, is constrained by the inner optimization problem of REMOS, which may become unsolvable in a reasonable time. A new feature extraction method based on frequency filtering was proposed to avoid this problem.Los actuales sistemas de reconocimiento del habla muestran a menudo una tasa de error aceptable si la voz es registrada por micr ofonos próximos a la boca del hablante, en un entorno controlado y libre de ruido. Sin embargo, el uso de estos micr ofonos puede ser demasiado restrictivo en muchas aplicaciones. Alternativamente, se pueden emplear micr ofonos distantes, los cuales a menudo se ubican a varios metros del hablante. Esta con guraci on es menos intrusiva ya que el hablante no tiene que llevar encima ning un micr ofono, pero el rendimiento del reconocimiento autom atico del habla (ASR, del ingl es Automatic Speech Recognition) en dicho caso se ve fuertemente afectado por el ruido y la reverberaci on. Esta tesis se enfoca a aplicaciones ASR en el entorno de una sala, donde la reverberaci on es la causa predominante de distorsi on y se considera tanto el caso de un solo micr ofono como el de m ultiples micr ofonos. Si el habla es grabada en paralelo por varios micr ofonos distribuidos arbitrariamente en la sala, el grado de distorsi on puede variar de un canal a otro. Las diferencias de calidad entre las señales grabadas pueden ser m as acentuadas si dichos micr ofonos muestran diferentes características y colocaciones: unos en las paredes, otros sobre la mesa, u otros integrados en los dispositivos de comunicaci on de las personas presentes en la sala. En dicho escenario el sistema ASR se puede bene ciar enormemente de la utilizaci on de la señal con mayor calidad para el reconocimiento. Para hallar dicha señal se han propuesto diversas t ecnicas, denominadas CS (del ingl es Channel Selection), las cuales se discuten detalladament en esta tesis. De hecho, la selecci on de canal busca ranquear las señales conforme a su calidad desde la perspectiva ASR. Para crear tal ranquin se necesita una medida que tanto estime la calidad intr nseca de una selal, como lo bien que esta se ajusta a los modelos ac usticos del sistema de reconocimiento. En esta tesis proporcionamos un resumen de las medidas CS hasta ahora presentadas en la literatura, compar andolas experimentalmente. Diversas nuevas t ecnicas son presentadas que superan las t ecnicas iniciales en cuanto a exactitud de reconocimiento y/o e ciencia computacional. Tambi en se propone una combinaci on de diferentes medidas CS para incrementar la exactitud de reconocimiento, o para reducir la carga computacional sin ninguna p erdida signi cativa de rendimiento. Adem as mostramos que la CS puede ser empleada junto con otras t ecnicas robustas de ASR, tales como matched condition training o la normalizaci on de la varianza y la media, y que las mejoras de reconocimiento de ambas aproximaciones son hasta cierto punto acumulativas. Una versi on online en tiempo real del m etodo de selecci on de canal basado en la varianza del speech sub-band envelopes, que fue desarrolladas en esta tesis, fue diseñada e implementada en una sala inteligente. Reportamos una mejora signi cativa en el rendimiento del reconocimiento al evaluar experimentalmente grabaciones reales de micr ofonos no pr oximos a la boca con hablantes en movimiento. La otra contribuci on de esta tesis, que no requiere m ultiples micr ofonos, fue desarrollada en colaboraci on con los colegas del departamento de Comunicaciones Multimedia y Procesamiento de Señales de la Universidad de Erlangen-Nuremberg, Erlangen, Alemania. Trata sobre el problema de extracci on de caracter sticas en REMOS (del ingl es REverberation MOdeling for Speech recognition). REMOS es un marco conceptual gen erico para el reconocimiento robusto del habla con micr ofonos lejanos. El uso de los m etodos convencionales para obtener los elementos decorrelados del vector de caracter sticas, como la transformada coseno discreta, est a limitado por el problema de optimizaci on inherente a REMOS, lo que har a que, utilizando las herramientas convencionales, se volviese un problema irresoluble en un tiempo razonable. Para resolver este problema hemos desarrollado un nuevo m etodo de extracci on de caracter sticas basado en fi ltrado frecuencialEls sistemes actuals de reconeixement de la parla mostren sovint una taxa d'error acceptable si la veu es registrada amb micr ofons pr oxims a la boca del parlant, en un entorn controlat i lliure de soroll. No obstant, l' us d'aquests micr ofons pot ser massa restrictiu en moltes aplicacions. Alternativament, es poden utilitzar micr ofons distants, els quals sovint s on ubicats a diversos metres del parlant. Aquesta con guraci o es menys intrusiva, ja que el parlant no ha de portar a sobre cap micr ofon, per o el rendiment del reconeixement autom atic de la parla (ASR, de l'angl es Automatic Speech Recognition) en aquest cas es veu fortament afectat pel soroll i la reverberaci o. Aquesta tesi s'enfoca a aplicacions ASR en un ambient de sala, on la reverberaci o es la causa predominant de distorsi o i es considera tant el cas d'un sol micr ofon com el de m ultiples micr ofons. Si la parla es gravada en paral lel per diversos micr ofons distribuï ts arbitràriament a la sala, el grau de distorsi o pot variar d'un canal a l'altre. Les difer encies en qualitat entre els senyals enregistrats poden ser m es accentuades si els micr ofons tenen diferents caracter stiques i col locacions: uns a les parets, altres sobre la taula, o b e altres integrats en els aparells de comunicaci o de les persones presents a la sala. En un escenari com aquest, el sistema ASR es pot bene ciar enormement de l'utilitzaci o del senyal de m es qualitat per al reconeixement. Per a trobar aquest senyal s'han proposat diverses t ecniques, anomenades CS (de l'angl es Channel Selection), les quals es discuteixen detalladament en aquesta tesi. De fet, la selecci o de canal busca ordenar els senyals conforme a la seva qualitat des de la perspectiva ASR. Per crear tal r anquing es necessita una mesura que estimi la qualitat intr nseca d'un senyal, o b e una que valori com de b e aquest s'ajusta als models ac ustics del sistema de reconeixement. En aquesta tesi proporcionem un resum de les mesures CS ns ara presentades en la literatura, comparant-les experimentalment. A m es, es presenten diverses noves t ecniques que superen les anteriors en termes d'exactitud de reconeixement i / o e ci encia computacional. Tamb e es proposa una combinaci o de diferents mesures CS amb l'objectiu d'incrementar l'exactitud del reconeixement, o per reduir la c arrega computacional sense cap p erdua signi cativa de rendiment. A m es mostrem que la CS pot ser utilitzada juntament amb altres t ecniques robustes d'ASR, com ara matched condition training o la normalitzaci o de la varian ca i la mitjana, i que les millores de reconeixement de les dues aproximacions s on ns a cert punt acumulatives. Una versi o online en temps real del m etode de selecci o de canal basat en la varian ca de les envolvents sub-banda de la parla, desenvolupada en aquesta tesi, va ser dissenyada i implementada en una sala intel ligent. A l'hora d'avaluar experimentalment gravacions reals de micr ofons no pr oxims a la boca amb parlants en moviment, es va observar una millora signi cativa en el rendiment del reconeixement. L'altra contribuci o d'aquesta tesi, que no requereix m ultiples micr ofons, va ser desenvolupada en col laboraci o amb els col legues del departament de Comunicacions Multimedia i Processament de Senyals de la Universitat de Erlangen-Nuremberg, Erlangen, Alemanya. Tracta sobre el problema d'extracci o de caracter stiques a REMOS (de l'angl es REverberation MOdeling for Speech recognition). REMOS es un marc conceptual gen eric per al reconeixement robust de la parla amb micr ofons llunyans. L' us dels m etodes convencionals per obtenir els elements decorrelats del vector de caracter stiques, com ara la transformada cosinus discreta, est a limitat pel problema d'optimitzaci o inherent a REMOS. Aquest faria que, utilitzant les eines convencionals, es torn es un problema irresoluble en un temps raonable. Per resoldre aquest problema hem desenvolupat un nou m etode d'extracci o de caracter ístiques basat en fi ltrat frecuencial

Distant Speech Recognition of Natural Spontaneous Multi-party Conversations

Distant speech recognition (DSR) has gained wide interest recently. While deep networks keep improving ASR overall, the performance gap remains between using close-talking recordings and distant recordings. Therefore the work in this thesis aims at providing some insights for further improvement of DSR performance. The investigation starts with collecting the first multi-microphone and multi-media corpus of natural spontaneous multi-party conversations in native English with the speaker location tracked, i.e. the Sheffield Wargame Corpus (SWC). The state-of-the-art recognition systems with the acoustic models trained standalone and adapted both show word error rates (WERs) above 40% on headset recordings and above 70% on distant recordings. A comparison between SWC and AMI corpus suggests a few unique properties in the real natural spontaneous conversations, e.g. the very short utterances and the emotional speech. Further experimental analysis based on simulated data and real data quantifies the impact of such influence factors on DSR performance, and illustrates the complex interaction among multiple factors which makes the treatment of each influence factor much more difficult. The reverberation factor is studied further. It is shown that the reverberation effect on speech features could be accurately modelled with a temporal convolution in the complex spectrogram domain. Based on that a polynomial reverberation score is proposed to measure the distortion level of short utterances. Compared to existing reverberation metrics like C50, it avoids a rigid early-late-reverberation partition without compromising the performance on ranking the reverberation level of recording environments and channels. Furthermore, the existing reverberation measurement is signal independent thus unable to accurately estimate the reverberation distortion level in short recordings. Inspired by the phonetic analysis on the reverberation distortion via self-masking and overlap-masking, a novel partition of reverberation distortion into the intra-phone smearing and the inter-phone smearing is proposed, so that the reverberation distortion level is first estimated on each part and then combined