A Soft Computing Based Approach for Multi-Accent Classification in IVR Systems

A speaker's accent is the most important factor affecting the performance of Natural Language Call Routing (NLCR) systems because accents vary widely, even within the same country or community. This variation also occurs when non-native speakers start to learn a second language, the substitution of native language phonology being a common process. Such substitution leads to fuzziness between the phoneme boundaries and phoneme classes, which reduces out-of-class variations, and increases the similarities between the different sets of phonemes. Thus, this fuzziness is the main cause of reduced NLCR system performance. The main requirement for commercial enterprises using an NLCR system is to have a robust NLCR system that provides call understanding and routing to appropriate destinations. The chief motivation for this present work is to develop an NLCR system that eliminates multilayered menus and employs a sophisticated speaker accent-based automated voice response system around the clock. Currently, NLCRs are not fully equipped with accent classification capability. Our main objective is to develop both speaker-independent and speaker-dependent accent classification systems that understand a caller's query, classify the caller's accent, and route the call to the acoustic model that has been thoroughly trained on a database of speech utterances recorded by such speakers. In the field of accent classification, the dominant approaches are the Gaussian Mixture Model (GMM) and Hidden Markov Model (HMM). Of the two, GMM is the most widely implemented for accent classification. However, GMM performance depends on the initial partitions and number of Gaussian mixtures, both of which can reduce performance if poorly chosen. To overcome these shortcomings, we propose a speaker-independent accent classification system based on a distance metric learning approach and evolution strategy. This approach depends on side information from dissimilar pairs of accent groups to transfer data points to a new feature space where the Euclidean distances between similar and dissimilar points are at their minimum and maximum, respectively. Finally, a Non-dominated Sorting Evolution Strategy (NSES)-based k-means clustering algorithm is employed on the training data set processed by the distance metric learning approach. The main objectives of the NSES-based k-means approach are to find the cluster centroids as well as the optimal number of clusters for a GMM classifier. In the case of a speaker-dependent application, a new method is proposed based on the fuzzy canonical correlation analysis to find appropriate Gaussian mixtures for a GMM-based accent classification system. In our proposed method, we implement a fuzzy clustering approach to minimize the within-group sum-of-square-error and canonical correlation analysis to maximize the correlation between the speech feature vectors and cluster centroids. We conducted a number of experiments using the TIMIT database, the speech accent archive, and the foreign accent English databases for evaluating the performance of speaker-independent and speaker-dependent applications. Assessment of the applications and analysis shows that our proposed methodologies outperform the HMM, GMM, vector quantization GMM, and radial basis neural networks

Robust Speaker-Adaptive HMM-based Text-to-Speech Synthesis

This paper describes a speaker-adaptive HMM-based speech synthesis system. The new system, called ``HTS-2007,'' employs speaker adaptation (CSMAPLR+MAP), feature-space adaptive training, mixed-gender modeling, and full-covariance modeling using CSMAPLR transforms, in addition to several other techniques that have proved effective in our previous systems. Subjective evaluation results show that the new system generates significantly better quality synthetic speech than speaker-dependent approaches with realistic amounts of speech data, and that it bears comparison with speaker-dependent approaches even when large amounts of speech data are available. In addition, a comparison study with several speech synthesis techniques shows the new system is very robust: It is able to build voices from less-than-ideal speech data and synthesize good-quality speech even for out-of-domain sentences

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

A cost, complexity and performance comparison of two automatic language identification architectures

This dissertation investigates the cost-complexity-performance relationship between two automatic language identification systems. The first is a state-of-the-art archi¬tecture, trained on about three hours of phonetically hand-labelled telephone speech obtained from the recognised OGLTS corpus. The second system, introduced by our¬selves, is a simpler design with a smaller, less complex parameter space. It is a vector quantisation-based approach which bears some resemblance to a system suggested by Sugiyama. Though trained on the same data, it has no need for any labels and is therefore less costly. A number of experiments are performed to find quasi-optimal parameters for the two systems. In further experiments the systems are evaluated and compared on a set of ten two-language tasks, spanning five languages. The more com¬plex system is shown to have a substantial performance advantage over the simpler design - 81% versus 65% on 40 seconds of speech. However, both results are well under reported state-of-the-art performance of 94% and would suggest that our systems can benefit from additional attention to implementation detail and optimisation of various parameters. Given the above, our suggested architecture may potentially provide an adequate solution where the high development cost associated with state-of-the-art technology and the necessary training corpora are prohibitive.Dissertation (M Eng (Computer Engineering))--University of Pretoria, 2006.Electrical, Electronic and Computer Engineeringunrestricte

Robust Speaker-Adaptive HMM-Based Text-to-Speech Synthesis

AbstractWe present an algorithm for solving the radiative transfer problem on massively parallel computers using adaptive mesh refinement and domain decomposition. The solver is based on the method of characteristics which requires an adaptive raytracer that integrates the equation of radiative transfer. The radiation field is split into local and global components which are handled separately to overcome the non-locality problem. The solver is implemented in the framework of the magneto-hydrodynamics code FLASH and is coupled by an operator splitting step. The goal is the study of radiation in the context of star formation simulations with a focus on early disc formation and evolution. This requires a proper treatment of radiation physics that covers both the optically thin as well as the optically thick regimes and the transition region in particular. We successfully show the accuracy and feasibility of our method in a series of standard radiative transfer problems and two 3D collapse simulations resembling the early stages of protostar and disc formation

Exploring Speech Technologies for Language Learning

The teaching of the pronunciation of any foreign language must encompass both segmental and suprasegmental aspects of speech. In computational terms, the two levels of language learning activities can be decomposed at least into phonemic aspects, which include the correct pronunciation of single phonemes and the co-articulation of phonemes into higher phonological units; as well as prosodic aspects which include  the correct position of stress at word level;  the alternation of stress and unstressed syllables in terms of compensation and vowel reduction;  the correct position of sentence accent;  the generation of the adequate rhymth from the interleaving of stress, accent, and phonological rules;  the generation of adequate intonational pattern for each utterance related to communicative functions; As appears from above, for a student to communicate intelligibly and as close as possible to native-speaker's pronunciation, prosody is very important [3]. We also assume that an incorrect prosody may hamper communication from taking place and this may be regarded a strong motivation for having the teaching of Prosody as an integral part of any language course. From our point of view it is much more important to stress the achievement of successful communication as the main objective of a second language learner rather than the overcoming of what has been termed “foreign accent”, which can be deemed as a secondary goal. In any case, the two goals are certainly not coincident even though they may be overlapping in some cases. We will discuss about these matter in the following sections. All prosodic questions related to “rhythm” will be discussed in the first section of this chapter. In [4] the author argues in favour of prosodic aids, in particular because a strong placement of word stress may impair understanding from the listener’s point of view of the word being pronounced. He also argues in favour of acquiring correct timing of phonological units to overcome the impression of “foreign accent” which may ensue from an incorrect distribution of stressed vs. unstressed stretches of linguistic units such as syllables or metric feet. Timing is not to be confused with speaking rate which need not be increased forcefully to give the impression of a good fluency: trying to increase speaking rate may result in lower intelligibility. The question of “foreign accent” is also discussed at length in (Jilka M., 1999). This work is particularly relevant as far as intonational features of a learner of a second language which we will address in the second section of this chapter. Correcting the Intonational Foreign Accent (hence IFA) is an important component of a Prosodic Module for self-learning activities, as categorical aspects of the intonation of the two languages in contact, L1 and L2 are far apart and thus neatly distinguishable. Choice of the two languages in contact is determined mainly by the fact that the distance in prosodic terms between English and Italian is maximal, according to (Ramus, F. and J. Mehler, 1999; Ramus F., et al., 1999)

The recognition of New Zealand English closing diphthongs using time-delay neural networks

As a step towards the development of a modular time-delay neural network (TDNN) for recognizing phonemes realized with a New Zealand English accent, this thesis focuses on the development of an expert module for closing diphthong recognition. The performances of traditional and squad-based expert modules are compared speaker-dependently for two New Zealand English speakers (one male and one female). Examples of each kind of expert module are formed from one of three types of TDNN, referred to as basic-token TDNN, extended-token TDNN and sequence-token TDNN. Of the traditional expert modules tested, those comprising extended-token TDNNs are found to afford the best performance compromises and are, therefore, preferable if a traditional expert module is to be used. Comparing the traditional and squad-based expert modules tested, the latter afford significantly better recognition and/or false-positive error performances than the former, irrespective of the type of TDNN used. Consequently, it is concluded that squad-based expert modules are preferable to their traditional counterparts for closing diphthong recognition. Of the squad-based expert modules tested, those comprising sequence-token TDNNs are found to afford consistently better false-positive error performances than those comprising basic- or extended-token TDNNs, while similar recognition performances are afforded by all. Consequently, squad-based expert modules comprising sequence-token TDNNs are recommended as the preferred method of recognizing closing diphthongs realized with a New Zealand accent. This thesis also presents results demonstrating that squad-based expert modules comprising sequence-token TDNN s may be trained to accommodate multiple speakers and in a manner capable of handling both uncorrupted and highly corrupted speech utterances

Pronunciation modelling in end-to-end text-to-speech synthesis

Sequence-to-sequence (S2S) models in text-to-speech synthesis (TTS) can achieve high-quality naturalness scores without extensive processing of text-input. Since S2S models have been proposed in multiple aspects of the TTS pipeline, the field has focused on embedding the pipeline toward End-to-End (E2E-) TTS where a waveform is predicted directly from a sequence of text or phone characters. Early work on E2ETTS in English, such as Char2Wav [1] and Tacotron [2], suggested that phonetisation (lexicon-lookup and/or G2P modelling) could be implicitly learnt in a text-encoder during training. The benefits of a learned text encoding include improved modelling of phonetic context, which make contextual linguistic features traditionally used in TTS pipelines redundant [3]. Subsequent work on E2E-TTS has since shown similar naturalness scores with text- or phone-input (e.g. as in [4]). Successful modelling of phonetic context has led some to question the benefit of using phone- instead of text-input altogether (see [5]). The use of text-input brings into question the value of the pronunciation lexicon in E2E-TTS. Without phone-input, a S2S encoder learns an implicit grapheme-tophoneme (G2P) model from text-audio pairs during training. With common datasets for E2E-TTS in English, I simulated implicit G2P models, finding increased error rates compared to a traditional, lexicon-based G2P model. Ultimately, successful G2P generalisation is difficult for some words (e.g. foreign words and proper names) since the knowledge to disambiguate their pronunciations may not be provided by the local grapheme context and may require knowledge beyond that contained in sentence-level text-audio sequences. When test stimuli were selected according to G2P difficulty, increased mispronunciations in E2E-TTS with text-input were observed. Following the proposed benefits of subword decomposition in S2S modelling in other language tasks (e.g. neural machine translation), the effects of morphological decomposition were investigated on pronunciation modelling. Learning of the French post-lexical phenomenon liaison was also evaluated. With the goal of an inexpensive, large-scale evaluation of pronunciation modelling, the reliability of automatic speech recognition (ASR) to measure TTS intelligibility was investigated. A re-evaluation of 6 years of results from the Blizzard Challenge was conducted. ASR reliably found similar significant differences between systems as paid listeners in controlled conditions in English. An analysis of transcriptions for words exhibiting difficult-to-predict G2P relations was also conducted. The E2E-ASR Transformer model used was found to be unreliable in its transcription of difficult G2P relations due to homophonic transcription and incorrect transcription of words with difficult G2P relations. A further evaluation of representation mixing in Tacotron finds pronunciation correction is possible when mixing text- and phone-inputs. The thesis concludes that there is still a place for the pronunciation lexicon in E2E-TTS as a pronunciation guide since it can provide assurances that G2P generalisation cannot

A study on reusing resources of speech synthesis for closely-related languages

This thesis describes research on building a text-to-speech (TTS) framework that can accommodate the lack of linguistic information of under-resource languages by using existing resources from another language. It describes the adaptation process required when such limited resource is used. The main natural languages involved in this research are Malay and Iban language. The thesis includes a study on grapheme to phoneme mapping and the substitution of phonemes. A set of substitution matrices is presented which show the phoneme confusion in term of perception among respondents. The experiments conducted study the intelligibility as well as perception based on context of utterances. The study on the phonetic prosody is then presented and compared to the Klatt duration model. This is to find the similarities of cross language duration model if one exists. Then a comparative study of Iban native speaker with an Iban polyglot TTS using Malay resources is presented. This is to confirm that the prosody of Malay can be used to generate Iban synthesised speech. The central hypothesis of this thesis is that by using a closely-related language resource, a natural sounding speech can be produced. The aim of this research was to show that by sticking to the indigenous language characteristics, it is possible to build a polyglot synthesised speech system even with insufficient speech resources