Objective assessment of speech intelligibility.

This thesis addresses the topic of objective speech intelligibility assessment. Speech intelligibility is becoming an important issue due most possibly to the rapid growth in digital communication systems in recent decades; as well as the increasing demand for security-based applications where intelligibility, rather than the overall quality, is the priority. Afterall, the loss of intelligibility means that communication does not exist. This research sets out to investigate the potential of automatic speech recognition (ASR) in intelligibility assessment, the motivation being the obvious link between word recognition and intelligibility. As a pre-cursor, quality measures are first considered since intelligibility is an attribute encompassed in overall quality. Here, 9 prominent quality measures including the state-of-the-art Perceptual Evaluation of Speech Quality (PESQ) are assessed. A large range of degradations are considered including additive noise and those introduced by coding and enhancement schemes. Experimental results show that apart from Weighted Spectral Slope (WSS), generally the quality scores from all other quality measures considered here correlate poorly with intelligibility. Poor correlations are observed especially when dealing with speech-like noises and degradations introduced by enhancement processes. ASR is then considered where various word recognition statistics, namely word accuracy, percentage correct, deletion, substitution and insertion are assessed as potential intelligibility measure. One critical contribution is the observation that there are links between different ASR statistics and different forms of degradation. Such links enable suitable statistics to be chosen for intelligibility assessment in different applications. In overall word accuracy from an ASR system trained on clean signals has the highest correlation with intelligibility. However, as is the case with quality measures, none of the ASR scores correlate well in the context of enhancement schemes since such processes are known to improve machine-based scores without necessarily improving intelligibility. This demonstrates the limitation of ASR in intelligibility assessment. As an extension to word modelling in ASR, one major contribution of this work relates to the novel use of a data-driven (DD) classifier in this context. The classifier is trained on intelligibility information and its output scores relate directly to intelligibility rather than indirectly through quality or ASR scores as in earlier attempts. A critical obstacle with the development of such a DD classifier is establishing the large amount of ground truth necessary for training. This leads to the next significant contribution, namely the proposal of a convenient strategy to generate potentially unlimited amounts of synthetic ground truth based on a well-supported hypothesis that speech processings rarely improve intelligibility. Subsequent contributions include the search for good features that could enhance classification accuracy. Scores given by quality measures and ASR are indicative of intelligibility hence could serve as potential features for the data-driven intelligibility classifier. Both are in investigated in this research and results show ASR-based features to be superior. A final contribution is a novel feature set based on the concept of anchor models where each anchor represents a chosen degradation. Signal intelligibility is characterised by the similarity between the degradation under test and a cohort of degradation anchors. The anchoring feature set leads to an average classification accuracy of 88% with synthetic ground truth and 82% with human ground truth evaluation sets. The latter compares favourably with 69% achieved by WSS (the best quality measure) and 68% by word accuracy from a clean-trained ASR (the best ASR-based measure) which are assessed on identical test sets

Closed-loop auditory-based representation for robust speech recognition

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Includes bibliographical references (p. 93-96).A closed-loop auditory based speech feature extraction algorithm is presented to address the problem of unseen noise for robust speech recognition. This closed-loop model is inspired by the possible role of the medial olivocochlear (MOC) efferent system of the human auditory periphery, which has been suggested in [6, 13, 42] to be important for human speech intelligibility in noisy environment. We propose that instead of using a fixed filter bank, the filters used in a feature extraction algorithm should be more flexible to adapt dynamically to different types of background noise. Therefore, in the closed-loop model, a feedback mechanism is designed to regulate the operating points of filters in the filter bank based on the background noise. The model is tested on a dataset created from TIDigits database. In this dataset, five kinds of noise are added to synthesize noisy speech. Compared with the standard MFCC extraction algorithm, the proposed closed-loop form of feature extraction algorithm provides 9.7%, 9.1% and 11.4% absolution word error rate reduction on average for three kinds of filter banks respectively.by Chia-ying Lee.S.M

Soft margin estimation for automatic speech recognition

In this study, a new discriminative learning framework, called soft margin estimation (SME), is proposed for estimating the parameters of continuous density hidden Markov models (HMMs). The proposed method makes direct use of the successful ideas of margin in support vector machines to improve generalization capability and decision feedback learning in discriminative training to enhance model separation in classifier design. SME directly maximizes the separation of competing models to enhance the testing samples to approach a correct decision if the deviation from training samples is within a safe margin. Frame and utterance selections are integrated into a unified framework to select the training utterances and frames critical for discriminating competing models. SME offers a flexible and rigorous framework to facilitate the incorporation of new margin-based optimization criteria into HMMs training. The choice of various loss functions is illustrated and different kinds of separation measures are defined under a unified SME framework. SME is also shown to be able to jointly optimize feature extraction and HMMs. Both the generalized probabilistic descent algorithm and the Extended Baum-Welch algorithm are applied to solve SME. SME has demonstrated its great advantage over other discriminative training methods in several speech recognition tasks. Tested on the TIDIGITS digit recognition task, the proposed SME approach achieves a string accuracy of 99.61%, the best result ever reported in literature. On the 5k-word Wall Street Journal task, SME reduced the word error rate (WER) from 5.06% of MLE models to 3.81%, with relative 25% WER reduction. This is the first attempt to show the effectiveness of margin-based acoustic modeling for large vocabulary continuous speech recognition in a HMMs framework. The generalization of SME was also well demonstrated on the Aurora 2 robust speech recognition task, with around 30% relative WER reduction from the clean-trained baseline.Ph.D.Committee Chair: Dr. Chin-Hui Lee; Committee Member: Dr. Anthony Joseph Yezzi; Committee Member: Dr. Biing-Hwang (Fred) Juang; Committee Member: Dr. Mark Clements; Committee Member: Dr. Ming Yua

Morphologically filtered power-normalized cochleograms as robust, biologically inspired features for ASR

In this paper, we present advances in the modeling of the masking behavior of the human auditory system (HAS) to enhance the robustness of the feature extraction stage in automatic speech recognition (ASR). The solution adopted is based on a nonlinear filtering of a spectro-temporal representation applied simultaneously to both frequency and time domains-as if it were an image-using mathematical morphology operations. A particularly important component of this architecture is the so-called structuring element (SE) that in the present contribution is designed as a single three-dimensional pattern using physiological facts, in such a way that closely resembles the masking phenomena taking place in the cochlea. A proper choice of spectro-temporal representation lends validity to the model throughout the whole frequency spectrum and intensity spans assuming the variability of the masking properties of the HAS in these two domains. The best results were achieved with the representation introduced as part of the power normalized cepstral coefficients (PNCC) together with a spectral subtraction step. This method has been tested on Aurora 2, Wall Street Journal and ISOLET databases including both classical hidden Markov model (HMM) and hybrid artificial neural networks (ANN)-HMM back-ends. In these, the proposed front-end analysis provides substantial and significant improvements compared to baseline techniques: up to 39.5% relative improvement compared to MFCC, and 18.7% compared to PNCC in the Aurora 2 database.This contribution has been supported by an Airbus Defense and Space Grant (Open Innovation - SAVIER) and Spanish Government-CICYT projects TEC2014-53390-P and TEC2014-61729-EX

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

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

Multi-candidate missing data imputation for robust speech recognition

The application of Missing Data Techniques (MDT) to increase the noise robustness of HMM/GMM-based large vocabulary speech recognizers is hampered by a large computational burden. The likelihood evaluations imply solving many constrained least squares (CLSQ) optimization problems. As an alternative, researchers have proposed frontend MDT or have made oversimplifying independence assumptions for the backend acoustic model. In this article, we propose a fast Multi-Candidate (MC) approach that solves the per-Gaussian CLSQ problems approximately by selecting the best from a small set of candidate solutions, which are generated as the MDT solutions on a reduced set of cluster Gaussians. Experiments show that the MC MDT runs equally fast as the uncompensated recognizer while achieving the accuracy of the full backend optimization approach. The experiments also show that exploiting the more accurate acoustic model of the backend does pay off in terms of accuracy when compared to frontend MDT. © 2012 Wang and Van hamme; licensee Springer.Wang Y., Van hamme H., ''Multi-candidate missing data imputation for robust speech recognition'', EURASIP journal on audio, speech, and music processing, vol. 17, 20 pp., 2012.status: publishe

Métodos discriminativos para la optimización de modelos en la Verificación del Hablante

La creciente necesidad de sistemas de autenticación seguros ha motivado el interés de algoritmos efectivos de Verificación de Hablante (VH). Dicha necesidad de algoritmos de alto rendimiento, capaces de obtener tasas de error bajas, ha abierto varias ramas de investigación. En este trabajo proponemos investigar, desde un punto de vista discriminativo, un conjunto de metodologías para mejorar el desempeño del estado del arte de los sistemas de VH. En un primer enfoque investigamos la optimización de los hiper-parámetros para explícitamente considerar el compromiso entre los errores de falsa aceptación y falso rechazo. El objetivo de la optimización se puede lograr maximizando el área bajo la curva conocida como ROC (Receiver Operating Characteristic) por sus siglas en inglés. Creemos que esta optimización de los parámetros no debe de estar limitada solo a un punto de operación y una estrategia más robusta es optimizar los parámetros para incrementar el área bajo la curva, AUC (Area Under the Curve por sus siglas en inglés) de modo que todos los puntos sean maximizados. Estudiaremos cómo optimizar los parámetros utilizando la representación matemática del área bajo la curva ROC basada en la estadística de Wilcoxon Mann Whitney (WMW) y el cálculo adecuado empleando el algoritmo de descendente probabilístico generalizado. Además, analizamos el efecto y mejoras en métricas como la curva detection error tradeoff (DET), el error conocido como Equal Error Rate (EER) y el valor mínimo de la función de detección de costo, minimum value of the detection cost function (minDCF) todos ellos por sue siglas en inglés. En un segundo enfoque, investigamos la señal de voz como una combinación de atributos que contienen información del hablante, del canal y el ruido. Los sistemas de verificación convencionales entrenan modelos únicos genéricos para todos los casos, y manejan las variaciones de estos atributos ya sea usando análisis de factores o no considerando esas variaciones de manera explícita. Proponemos una nueva metodología para particionar el espacio de los datos de acuerdo a estas carcterísticas y entrenar modelos por separado para cada partición. Las particiones se pueden obtener de acuerdo a cada atributo. En esta investigación mostraremos como entrenar efectivamente los modelos de manera discriminativa para maximizar la separación entre ellos. Además, el diseño de algoritimos robustos a las condiciones de ruido juegan un papel clave que permite a los sistemas de VH operar en condiciones reales. Proponemos extender nuestras metodologías para mitigar los efectos del ruido en esas condiciones. Para nuestro primer enfoque, en una situación donde el ruido se encuentre presente, el punto de operación puede no ser solo un punto, o puede existir un corrimiento de forma impredecible. Mostraremos como nuestra metodología de maximización del área bajo la curva ROC es más robusta que la usada por clasificadores convencionales incluso cuando el ruido no está explícitamente considerado. Además, podemos encontrar ruido a diferentes relación señal a ruido (SNR) que puede degradar el desempeño del sistema. Así, es factible considerar una descomposición eficiente de las señales de voz que tome en cuenta los diferentes atributos como son SNR, el ruido y el tipo de canal. Consideramos que en lugar de abordar el problema con un modelo unificado, una descomposición en particiones del espacio de características basado en atributos especiales puede proporcionar mejores resultados. Esos atributos pueden representar diferentes canales y condiciones de ruido. Hemos analizado el potencial de estas metodologías que permiten mejorar el desempeño del estado del arte de los sistemas reduciendo el error, y por otra parte controlar los puntos de operación y mitigar los efectos del ruido