Una estrategia de procesamiento automático del habla basada en la detección de atributos

State-of-the-art automatic speech and speaker recognition systems are often built with a pattern matching framework that has proven to achieve low recognition error rates for a variety of resource-rich tasks when the volume of speech and text examples to build statistical acoustic and language models is plentiful, and the speaker, acoustics and language conditions follow a rigid protocol. However, because of the “blackbox” top-down knowledge integration approach, such systems cannot easily leverage a rich set of knowledge sources already available in the literature on speech, acoustics and languages. In this paper, we present a bottom-up approach to knowledge integration, called automatic speech attribute transcription (ASAT), which is intended to be “knowledge-rich”, so that new and existing knowledge sources can be verified and integrated into current spoken language systems to improve recognition accuracy and system robustness. Since the ASAT framework offers a “divide-and-conquer” strategy and a “plug-andplay” game plan, it will facilitate a cooperative speech processing community that every researcher can contribute to, with a view to improving speech processing capabilities which are currently not easily accessible to researchers in the speech science community.Los sistemas más novedosos de reconocimiento automático de habla y de locutor suelen basarse en un sistema de coincidencia de patrones. Gracias a este modo de trabajo, se han obtenido unos bajos índices de error de reconocimiento para una variedad de tareas ricas en recursos, cuando se aporta una cantidad abundante de ejemplos de habla y texto para el entrenamiento estadístico de los modelos acústicos y de lenguaje, y siempre que el locutor y las condiciones acústicas y lingüísticas sigan un protocolo estricto. Sin embargo, debido a su aplicación de un proceso ciego de integración del conocimiento de arriba a abajo, dichos sistemas no pueden aprovechar fácilmente toda una serie de conocimientos ya disponibles en la literatura sobre el habla, la acústica y las lenguas. En este artículo presentamos una aproximación de abajo a arriba a la integración del conocimiento, llamada transcripción automática de atributos del habla (conocida en inglés como automatic speech attribute transcription, ASAT). Dicho enfoque pretende ser “rico en conocimiento”, con el fin de poder verificar las fuentes de conocimiento, tanto nuevas como ya existentes, e integrarlas en los actuales sistemas de lengua hablada para mejorar la precisión del reconocimiento y la robustez del sistema. Dado que ASAT ofrece una estrategia de tipo “divide y vencerás” y un plan de juego de “instalación y uso inmediato” (en inglés, plugand-play), esto facilitará una comunidad cooperativa de procesamiento del habla a la que todo investigador pueda contribuir con vistas a mejorar la capacidad de procesamiento del habla, que en la actualidad no es fácilmente accesible a los investigadores de la comunidad de las ciencias del habla

A Framework For Enhancing Speaker Age And Gender Classification By Using A New Feature Set And Deep Neural Network Architectures

Speaker age and gender classification is one of the most challenging problems in speech processing. Recently with developing technologies, identifying a speaker age and gender has become a necessity for speaker verification and identification systems such as identifying suspects in criminal cases, improving human-machine interaction, and adapting music for awaiting people queue. Although many studies have been carried out focusing on feature extraction and classifier design for improvement, classification accuracies are still not satisfactory. The key issue in identifying speaker’s age and gender is to generate robust features and to design an in-depth classifier. Age and gender information is concealed in speaker’s speech, which is liable for many factors such as, background noise, speech contents, and phonetic divergences. In this work, different methods are proposed to enhance the speaker age and gender classification based on the deep neural networks (DNNs) as a feature extractor and classifier. First, a model for generating new features from a DNN is proposed. The proposed method uses the Hidden Markov Model toolkit (HTK) tool to find tied-state triphones for all utterances, which are used as labels for the output layer in the DNN. The DNN with a bottleneck layer is trained in an unsupervised manner for calculating the initial weights between layers, then it is trained and tuned in a supervised manner to generate transformed mel-frequency cepstral coefficients (T-MFCCs). Second, the shared class labels method is introduced among misclassified classes to regularize the weights in DNN. Third, DNN-based speakers models using the SDC feature set is proposed. The speakers-aware model can capture the characteristics of the speaker age and gender more effectively than a model that represents a group of speakers. In addition, AGender-Tune system is proposed to classify the speaker age and gender by jointly fine-tuning two DNN models; the first model is pre-trained to classify the speaker age, and second model is pre-trained to classify the speaker gender. Moreover, the new T-MFCCs feature set is used as the input of a fusion model of two systems. The first system is the DNN-based class model and the second system is the DNN-based speaker model. Utilizing the T-MFCCs as input and fusing the final score with the score of a DNN-based class model enhanced the classification accuracies. Finally, the DNN-based speaker models are embedded into an AGender-Tune system to exploit the advantages of each method for a better speaker age and gender classification. The experimental results on a public challenging database showed the effectiveness of the proposed methods for enhancing the speaker age and gender classification and achieved the state of the art on this database

Automatic Screening of Childhood Speech Sound Disorders and Detection of Associated Pronunciation Errors

Speech disorders in children can affect their fluency and intelligibility. Delay in their diagnosis and treatment increases the risk of social impairment and learning disabilities. With the significant shortage of Speech and Language Pathologists (SLPs), there is an increasing interest in Computer-Aided Speech Therapy tools with automatic detection and diagnosis capability. However, the scarcity and unreliable annotation of disordered child speech corpora along with the high acoustic variations in the child speech data has impeded the development of reliable automatic detection and diagnosis of childhood speech sound disorders. Therefore, this thesis investigates two types of detection systems that can be achieved with minimum dependency on annotated mispronounced speech data. First, a novel approach that adopts paralinguistic features which represent the prosodic, spectral, and voice quality characteristics of the speech was proposed to perform segment- and subject-level classification of Typically Developing (TD) and Speech Sound Disordered (SSD) child speech using a binary Support Vector Machine (SVM) classifier. As paralinguistic features are both language- and content-independent, they can be extracted from an unannotated speech signal. Second, a novel Mispronunciation Detection and Diagnosis (MDD) approach was introduced to detect the pronunciation errors made due to SSDs and provide low-level diagnostic information that can be used in constructing formative feedback and a detailed diagnostic report. Unlike existing MDD methods where detection and diagnosis are performed at the phoneme level, the proposed method achieved MDD at the speech attribute level, namely the manners and places of articulations. The speech attribute features describe the involved articulators and their interactions when making a speech sound allowing a low-level description of the pronunciation error to be provided. Two novel methods to model speech attributes are further proposed in this thesis, a frame-based (phoneme-alignment) method leveraging the Multi-Task Learning (MTL) criterion and training a separate model for each attribute, and an alignment-free jointly-learnt method based on the Connectionist Temporal Classification (CTC) sequence to sequence criterion. The proposed techniques have been evaluated using standard and publicly accessible adult and child speech corpora, while the MDD method has been validated using L2 speech corpora