Extended phone log-likelihood ratio features and acoustic-based I-vectors for language recognition

This paper presents new techniques with relevant improvements added to the primary system presented by our group to the Albayzin 2012 LRE competition, where the use of any additional corpora for training or optimizing the models was forbidden. In this work, we present the incorporation of an additional phonotactic subsystem based on the use of phone log-likelihood ratio features (PLLR) extracted from different phonotactic recognizers that contributes to improve the accuracy of the system in a 21.4% in terms of Cavg (we also present results for the official metric during the evaluation, Fact). We will present how using these features at the phone state level provides significant improvements, when used together with dimensionality reduction techniques, especially PCA. We have also experimented with applying alternative SDC-like configurations on these PLLR features with additional improvements. Also, we will describe some modifications to the MFCC-based acoustic i-vector system which have also contributed to additional improvements. The final fused system outperformed the baseline in 27.4% in Cavg

Language recognition using phonotactic-based shifted delta coefficients and multiple phone recognizers

A new language recognition technique based on the application of the philosophy of the Shifted Delta Coefficients (SDC) to phone log-likelihood ratio features (PLLR) is described. The new methodology allows the incorporation of long-span phonetic information at a frame-by-frame level while dealing with the temporal length of each phone unit. The proposed features are used to train an i-vector based system and tested on the Albayzin LRE 2012 dataset. The results show a relative improvement of 33.3% in Cavg in comparison with different state-of-the-art acoustic i-vector based systems. On the other hand, the integration of parallel phone ASR systems where each one is used to generate multiple PLLR coefficients which are stacked together and then projected into a reduced dimension are also presented. Finally, the paper shows how the incorporation of state information from the phone ASR contributes to provide additional improvements and how the fusion with the other acoustic and phonotactic systems provides an important improvement of 25.8% over the system presented during the competition

Subspace Gaussian Mixture Models for Language Identification and Dysarthric Speech Intelligibility Assessment

En esta Tesis se ha investigado la aplicación de técnicas de modelado de subespacios de mezclas de Gaussianas en dos problemas relacionados con las tecnologías del habla, como son la identificación automática de idioma (LID, por sus siglas en inglés) y la evaluación automática de inteligibilidad en el habla de personas con disartria. Una de las técnicas más importantes estudiadas es el análisis factorial conjunto (JFA, por sus siglas en inglés). JFA es, en esencia, un modelo de mezclas de Gaussianas en el que la media de cada componente se expresa como una suma de factores de dimensión reducida, y donde cada factor representa una contribución diferente a la señal de audio. Esta factorización nos permite compensar nuestros modelos frente a contribuciones indeseadas presentes en la señal, como la información de canal. JFA se ha investigado como clasficador y como extractor de parámetros. En esta última aproximación se modela un solo factor que representa todas las contribuciones presentes en la señal. Los puntos en este subespacio se denominan i-Vectors. Así, un i-Vector es un vector de baja dimensión que representa una grabación de audio. Los i-Vectors han resultado ser muy útiles como vector de características para representar señales en diferentes problemas relacionados con el aprendizaje de máquinas. En relación al problema de LID, se han investigado dos sistemas diferentes de acuerdo al tipo de información extraída de la señal. En el primero, la señal se parametriza en vectores acústicos con información espectral a corto plazo. En este caso, observamos mejoras de hasta un 50% con el sistema basado en i-Vectors respecto al sistema que utilizaba JFA como clasificador. Se comprobó que el subespacio de canal del modelo JFA también contenía información del idioma, mientras que con los i-Vectors no se descarta ningún tipo de información, y además, son útiles para mitigar diferencias entre los datos de entrenamiento y de evaluación. En la fase de clasificación, los i-Vectors de cada idioma se modelaron con una distribución Gaussiana en la que la matriz de covarianza era común para todos. Este método es simple y rápido, y no requiere de ningún post-procesado de los i-Vectors. En el segundo sistema, se introdujo el uso de información prosódica y formántica en un sistema de LID basado en i-Vectors. La precisión de éste estaba por debajo de la del sistema acústico. Sin embargo, los dos sistemas son complementarios, y se obtuvo hasta un 20% de mejora con la fusión de los dos respecto al sistema acústico solo. Tras los buenos resultados obtenidos para LID, y dado que, teóricamente, los i-Vectors capturan toda la información presente en la señal, decidimos usarlos para la evaluar de manera automática la inteligibilidad en el habla de personas con disartria. Los logopedas están muy interesados en esta tecnología porque permitiría evaluar a sus pacientes de una manera objetiva y consistente. En este caso, los i-Vectors se obtuvieron a partir de información espectral a corto plazo de la señal, y la inteligibilidad se calculó a partir de los i-Vectors obtenidos para un conjunto de palabras dichas por el locutor evaluado. Comprobamos que los resultados eran mucho mejores si en el entrenamiento del sistema se incorporaban datos de la persona que iba a ser evaluada. No obstante, esta limitación podría aliviarse utilizando una mayor cantidad de datos para entrenar el sistema.In this Thesis, we investigated how to effciently apply subspace Gaussian mixture modeling techniques onto two speech technology problems, namely automatic spoken language identification (LID) and automatic intelligibility assessment of dysarthric speech. One of the most important of such techniques in this Thesis was joint factor analysis (JFA). JFA is essentially a Gaussian mixture model where the mean of the components is expressed as a sum of low-dimension factors that represent different contributions to the speech signal. This factorization makes it possible to compensate for undesired sources of variability, like the channel. JFA was investigated as final classiffer and as feature extractor. In the latter approach, a single subspace including all sources of variability is trained, and points in this subspace are known as i-Vectors. Thus, one i-Vector is defined as a low-dimension representation of a single utterance, and they are a very powerful feature for different machine learning problems. We have investigated two different LID systems according to the type of features extracted from speech. First, we extracted acoustic features representing short-time spectral information. In this case, we observed relative improvements with i-Vectors with respect to JFA of up to 50%. We realized that the channel subspace in a JFA model also contains language information whereas i-Vectors do not discard any language information, and moreover, they help to reduce mismatches between training and testing data. For classification, we modeled the i-Vectors of each language with a Gaussian distribution with covariance matrix shared among languages. This method is simple and fast, and it worked well without any post-processing. Second, we introduced the use of prosodic and formant information with the i-Vectors system. The performance was below the acoustic system but both were found to be complementary and we obtained up to a 20% relative improvement with the fusion with respect to the acoustic system alone. Given the success in LID and the fact that i-Vectors capture all the information that is present in the data, we decided to use i-Vectors for other tasks, specifically, the assessment of speech intelligibility in speakers with different types of dysarthria. Speech therapists are very interested in this technology because it would allow them to objectively and consistently rate the intelligibility of their patients. In this case, the input features were extracted from short-term spectral information, and the intelligibility was assessed from the i-Vectors calculated from a set of words uttered by the tested speaker. We found that the performance was clearly much better if we had available data for training of the person that would use the application. We think that this limitation could be relaxed if we had larger databases for training. However, the recording process is not easy for people with disabilities, and it is difficult to obtain large datasets of dysarthric speakers open to the research community. Finally, the same system architecture for intelligibility assessment based on i-Vectors was used for predicting the accuracy that an automatic speech recognizer (ASR) system would obtain with dysarthric speakers. The only difference between both was the ground truth label set used for training. Predicting the performance response of an ASR system would increase the confidence of speech therapists in these systems and would diminish health related costs. The results were not as satisfactory as in the previous case, probably because an ASR is a complex system whose accuracy can be very difficult to be predicted only with acoustic information. Nonetheless, we think that we opened a door to an interesting research direction for the two problems

An Ordinal Approach to Affective Computing

Both depression prediction and emotion recognition systems are often based on ordinal ground truth due to subjectively annotated datasets. Yet, both have so far been posed as classification or regression problems. These naive approaches have fundamental issues because they are not focused on ordering, unlike ordinal regression, which is the most appropriate for truly ordinal ground truth. Ordinal regression to date offers comparatively fewer, more limited methods when compared with other branches in machine learning, and its usage has been limited to specific research domains. Accordingly, this thesis presents investigations into ordinal approaches for affective computing by describing a consistent framework to understand all ordinal system designs, proposing ordinal systems for large datasets, and introducing tools and principles to select suitable system designs and evaluation methods. First, three learning approaches are compared using the support vector framework to establish the empirical advantages of ordinal regression, which is lacking from the current literature. Results on depression and emotion corpora indicate that ordinal regression with proper tuning can improve existing depression and emotion systems. Ordinal logistic regression (OLR), which is an extension of logistic regression for ordinal scales, contributes to a number of model structures, from which the best structure must be chosen. Exploiting the newly proposed computationally efficient greedy algorithm for model structure selection (GREP), OLR outperformed or was comparable with state-of-the-art depression systems on two benchmark depression speech datasets. Deep learning has dominated many affective computing fields, and hence ordinal deep learning is an attractive prospect. However, it is under-studied even in the machine learning literature, which motivates an in-depth analysis of appropriate network architectures and loss functions. One of the significant outcomes of this analysis is the introduction of RankCNet, a novel ordinal network which utilises a surrogate loss function of rank correlation. Not only the modelling algorithm but the choice of evaluation measure depends on the nature of the ground truth. Rank correlation measures, which are sensitive to ordering, are more apt for ordinal problems than common classification or regression measures that ignore ordering information. Although rank-based evaluation for ordinal problems is not new, so far in affective computing, ordinality of the ground truth has been widely ignored during evaluation. Hence, a systematic analysis in the affective computing context is presented, to provide clarity and encourage careful choice of evaluation measures. Another contribution is a neural network framework with a novel multi-term loss function to assess the ordinality of ordinally-annotated datasets, which can guide the selection of suitable learning and evaluation methods. Experiments on multiple synthetic and affective speech datasets reveal that the proposed system can offer reliable and meaningful predictions about the ordinality of a given dataset. Overall, the novel contributions and findings presented in this thesis not only improve prediction accuracy but also encourage future research towards ordinal affective computing: a different paradigm, but often the most appropriate

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