Computer Models of Speech Using Fuzzy Algorithms

Because emotions are such a significant and integral part of being human, understanding them and knowing how to react to the emotions of others is a fundamental requirement for successful social interaction. We recognize emotions primarily through speech and facial expression. The topic is gaining importance in academic research triggered by research in new techniques such as identifying emotions based on speech context. This investigates the relationship between emotions and the content of our speech. This paper proposes how emotion in speech and facial expression can be recognized in real time using a framework consisting of mobile phone technology backed by cloud computing. This functionality was developed and built into a mobile phone application. Currently, the application works on any Android smartphone to detect and identify emotions in real time. The results are expressed as a percentage of all the possible emotions, such as sad, happy, fear, surprise, anger and so on. The results of the experiment confirm that face and speech emotion recognition was conducted successfully using a smartphone. It was correct in 97.26% of instances when used with standard corpora: a.Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) b.the Surrey Audio-Visual Expressed Emotion (SAVEE)

Automated Facial Expression and Speech Emotion Recognition App Development on Smart Phones using Cloud Computing

Recognition of connected words can be performed by segmenting the word string automatically into single-word components which are then classified by a single-word recognition system. We propose and investigate a speaker-independent segmentation procedure which is based completely on statistical principles. An estimation algorithm, adapted to the statistical data of the signal parameters, determines the word boundaries. The statistical data are computed from vocabulary-dependent speech samples of different speakers. The segmentation procedure, which operates independently of the single-word recognizer, has been tested with connected digits. The results show that an estimation algorithm based on quadratic polynomials yields a very reliable segmentation. The segmentation procedure is also well suited for a speech input where the number of words in a word string is not known to the recognition system. Based on the above segmentation procedure, we have carried out several recognition experiments on two-to-four-digit strings. The investigations show that the proposed segmentation algorithm provides an efficient tool to tackle the effects of coarticulation between adjacent words. We present a training procedure which automatically adapts the classifier to the speaker-dependent effects of coarticulation.

http://ieeexplore.ieee.org/iel6/29/26178/01164143.pdf

A segmentation algorithm for connected word recognition based on estimation principles

A fully integrated speech recognition LSI has been developed. The speech recognition LSI can recognize a speaker-dependent vocabulary of about 200 isolated words with high accuracy in real time, using several memories, which are a phoneme template memory, word dictionary memory, and work memory. This LSI is designed to perform the total speech recognition processing, including the endpoint detection of the input utterance in a self-contained manner. With the pipelined structure of the function blocks, highly efficient parallel operations are achieved. Furthermore, satisfactory testability is assured with a scan path technique. The speech recognition LSI is fabricated with 2 /spl mu/m E/D NMOS process technology, employing two aluminium interconnection layers and a high resistivity poly-Si layer.

A single-chip self-contained speech recognizer

http://ieeexplore.ieee.org/iel5/5468824/5468825/05468862.pdf

A Single Chip Self-Contained Speech Recognizer

Construction and performance of a machine for recognizing spoken connected words are described. In this machine, a new method for connected word recognition, namely inverse dynamic programming (DP) matching, is adopted. Two kinds of DP matching techniques are used in the inverse DP matching, one of which is the usual DP matching and the other is matching performed in a time reverse mode, starting from the end of speech. Combining the similarities obtained by these two kinds of matching, the similarities between input speech and word sequences are computed. Also a technique for rejecting candidates is used in the machine to reduce computation amount. The machine performance is tested by 1400 samples of connected digits. The recognition rate of 99.3% is obtained.

R. Nakatsu

Papers

A speech recognition machine for connected words