Abstract
This paper presents the recognition of “Baoule” spoken sentences, a language of C?te d’Ivoire. Several formalisms allow the modelling of an automatic speech recognition system. The one we used to realize our system is based on Hidden Markov Models (HMM) discreet. Our goal in this article is to present a system for the recognition of the Baoule word. We present three classical problems and develop different algorithms able to resolve them. We then execute these algorithms with concrete examples.
Highlights
The speech recognition by machine has long been a research topic that fascinates the public and remains a challenge for specialists, and it has continued since to be at the heart of much research
The one we used to realize our system is based on Hidden Markov Models (HMM) discreet
We presented a method to separate phonemes contained in a speech signal
Summary
The speech recognition by machine has long been a research topic that fascinates the public and remains a challenge for specialists, and it has continued since to be at the heart of much research. These factors do not usually represent difficulties Our brain juggles these deformations of speech by taking into account, almost unconsciously, nonverbal and contextual elements that al-. We will consider all the consequences of M words that could match the signal A In this set of possible word sequences, we will choose the one (M) which is the most likely to maximize the P(M/A) probability that M is the correct interpretation of A, we note:. This equation is the key to the probabilistic approach to speech recognition. By integrating a Markov modeling, which has higher levels of language, it becomes possible to achieve a pronounced phrases discretely recognition system (i.e. in single word)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
