Using Broad Phonetic Group Experts for Improved Speech Recognition

Deep Learning of Speech Features for Improved Phonetic Recognition

Recently, a remarkable performance result of 23.0% Phone Error Rate (PER) on the TIMIT core test set was reported by applying Deep Belief Network (DBN) on phonetic recognition [1]. Despite the good performance reported, there is still substantial room for improvement in the reported design in order to achieve optimal results. In this letter, we present an improved but simple architecture for ph...

Using broad phonetic classes to guide search in automatic speech recognition

This work presents a novel framework to guide the Viterbi decoding process of a hidden Markov model based speech recognition system by means of broad phonetic classes. In a first step, decision trees are employed, along with frame and segment based attributes, in order to detect broad phonetic classes in the speech signal. Then, the detected phonetic classes are used to reinforce paths in the s...

Speaker Recognition and Broad Phonetic Groups

The aim of this study is to provide a quantitative assessment of the speaker discriminating properties of broad phonetic groups. GMM based approach to speaker modelling is used in conjunction with a phonetically handlabelled speech database (TIMIT) to produce broad phonetic group ranking based on speaker identification scores. The broad phonetic groups nasals and vowels were found to be particu...

Using Phonetic Context for Continuous Speech Recognition with Invariant Structure

Arabic Phonetic Dictionaries for Speech Recognition

Phonetic dictionaries are essential components of large-vocabulary speaker-independent speech recognition systems. This paper presents a rule-based technique to generate phonetic dictionaries for a large vocabulary Arabic speech recognition system. The system used conventional Arabic pronunciation rules, common pronunciation rules of Modern Standard Arabic, as well as some common dialectal case...