Arabic Continuous Speech Recognition System using Sphinx-4

Speech is the most natural form of human communication and speech processing has been one of the most exciting areas of the signal processing. Speech recognition technology has made it possible for computer to follow human voice commands and understand human languages. The main goal of speech recognition area is to develop techniques and systems for speech input to machine and treat this speech to be used in many applications. As Arabic is one of the most widely spoken languages in the world. Statistics show that it is the first language (mother-tongue) of 206 million native speakers ranked as fourth after Mandarin, Spanish and English. In spite of its importance, research effort on Arabic Automatic Speech Recognition (ASR) is unfortunately still inadequate[7]. This thesis proposes and describes an efficient and effective framework for designing and developing a speaker-independent continuous automatic Arabic speech recognition system based on a phonetically rich and balanced speech corpus. The developing Arabic speech recognition system is based on the Carnegie Mellon university Sphinx tools. To build the system, we develop three basic components. The dictionary which contains all possible phonetic pronunciations of any word in the domain vocabulary. The second one is the language model such a model tries to capture the properties of a sequence of words by means of a probability distribution, and to predict the next word in a speech sequence. The last one is the acoustic model which will be created by taking audio recordings of speech, and their text transcriptions, and using software to create statistical representations of the sounds that make up each word. The system use the rich and balanced database that contains 367 sentences, a total of 14232 words. The phonetic dictionary contains about 23,841 definitions corresponding to the database words. And the language model contains14233 mono-gram and 32813 bi-grams and 37771 tri-grams. The engine uses 3-emmiting states Hidden Markov Models (HMMs) for tri-phone-based acoustic models

Speech recognition system based on Hidden Markov Model concerning the Moroccan dialect DARIJA

In this work, we present a system for automatic speech recognition on the Moroccan dialect. We used the hidden Markov model to model the phonetic units corresponding to words taken from the training base. The results obtained are very encouraging given the size of the training set and the number of people taken to the registration. To demonstrate the flexibility of the hidden Markov model we conducted a comparison of results obtained by the latter and dynamic programming

On Developing an Automatic Speech Recognition System for Commonly used English Words in Indian English

Speech is one of the easiest and the fastest way to communicate. Recognition of speech by computer for various languages is a challenging task. The accuracy of Automatic speech recognition system (ASR) remains one of the key challenges, even after years of research. Accuracy varies due to speaker and language variability, vocabulary size and noise. Also, due to the design of speech recognition that is based on issues like- speech database, feature extraction techniques and performance evaluation. This paper aims to describe the development of a speaker-independent isolated automatic speech recognition system for Indian English language. The acoustic model is build using Carnegie Mellon University (CMU) Sphinx tools. The corpus used is based on Most Commonly used English words in everyday life. Speech database includes the recordings of 76 Punjabi Speakers (north-west Indian English accent). After testing, the system obtained an accuracy of 85.20 %, when trained using 128 GMMs (Gaussian Mixture Models)

Modelo Acústico y de Lenguaje del Idioma Español para el dialecto Cucuteño, Orientado al Reconocimiento Automático del Habla

 Context: Automatic speech recognition requires the development of language and acoustic models for different existing dialects. The purpose of this research is the training of an acoustic model, a statistical language model and a grammar language model for the Spanish language, specifically for the dialect of the city of San Jose de Cucuta, Colombia, that can be used in a command control system. Existing models for the Spanish language have problems in the recognition of the fundamental frequency and the spectral content, the accent, pronunciation, tone or simply the language model for Cucuta's dialect.Method: in this project, we used Raspberry Pi B+ embedded system with Raspbian operating system which is a Linux distribution and two open source software, namely CMU-Cambridge Statistical Language Modeling Toolkit from the University of Cambridge and CMU Sphinx from Carnegie Mellon University; these software are based on Hidden Markov Models for the calculation of voice parameters. Besides, we used 1913 recorded audios with the voice of people from San Jose de Cucuta and Norte de Santander department. These audios were used for training and testing the automatic speech recognition system.Results: we obtained a language model that consists of two files, one is the statistical language model (.lm), and the other is the jsgf grammar model (.jsgf). Regarding the acoustic component, two models were trained, one of them with an improved version which had a 100 % accuracy rate in the training results and 83 % accuracy rate in the audio tests for command recognition. Finally, we elaborated a manual for the creation of acoustic and language models with CMU Sphinx software.Conclusions: The number of participants in the training process of the language and acoustic models has a significant influence on the quality of the voice processing of the recognizer. The use of a large dictionary for the training process and a short dictionary with the command words for the implementation is important to get a better response of the automatic speech recognition system. Considering the accuracy rate above 80 % in the voice recognition tests, the proposed models are suitable for applications oriented to the assistance of visual or motion impairment people.  Contexto: El reconocimiento automático del habla requiere el desarrollo de modelos de lenguaje y modelos acústicos para los diferentes dialectos que existen. El objeto de esta investigación es el entrenamiento de un modelo acústico, un modelo de lenguaje estadístico y un modelo de lenguaje gramatical para el idioma español, específicamente para el dialecto de la ciudad de San José de Cúcuta, Colombia, que pueda ser utilizado en un sistema de control por comandos. Lo anterior motivado en las deficiencias que presentan los modelos existentes para el idioma español, para el reconocimiento de la frecuencia fundamental y contenido espectral, el acento, la pronunciación, el tono o simplemente al modelo de lenguaje de la variante dialéctica de esta región.Método: Este proyecto utiliza el sistema embebido Raspberry Pi B+ con el sistema operativo Raspbian que es una distribución de Linux, y los softwares de código abierto CMU-Cambridge Statistical Language Modeling toolkit de la Universidad de Cambridge y CMU Sphinx de la Universidad Carnegie Mellon; los cuales se basan en los modelos ocultos de Markov para el cálculo de los parámetros de voz. Además, se utilizaron 1913 audios grabados por locutores de la ciudad de San José de Cúcuta y el departamento de Norte de Santander para el entrenamiento y las pruebas del sistema de reconocimiento automático del habla.Resultados: Se obtuvo un modelo de lenguaje que consiste de dos archivos, uno de modelo de lenguaje estadístico (. lm), y uno de modelo gramatical (. jsgf). Con relación a la parte acústica se entrenaron dos modelos, uno de ellos con una versión mejorada que obtuvo una tasa de acierto en el reconocimiento de comandos del 100% en los datos de entrenamiento y de 83% en las pruebas de audio. Por último, se elaboró un manual para la creación de los modelos acústicos y de lenguaje con el software CMU Sphinx.  Conclusiones: El número de participantes en el proceso de entrenamiento de los modelos acústicos y de lenguaje influye significativamente en la calidad del procesamiento de voz del reconocedor. Para obtener una mejor respuesta del sistema de Reconocimiento Automático del Habla es importante usar un diccionario largo para la etapa de entrenamiento y un diccionario corto con las palabras de comando para la implementación del sistema. Teniendo en cuenta que en las pruebas de reconocimiento se obtuvo una tasa de éxito mayor al 80% es posible usar los modelos creados en el desarrollo de un sistema de Reconocimiento Automático del Habla para una aplicación orientada a la asistencia de personas con discapacidad visual o incapacidad de movimiento