Single- and two-layer perceptron models are adapted for experiments in isolated-word recognition. Direct (one-step) classification as well as several hierarchical. Isolated word recognition using Markov chain models. Abstract: The paper describes how Markov chains may be applied to speech recognition. In this. Isolated word recognition using phoneme-like templates. Abstract: This paper describes new technique for use in a word recognition system. This recognition.


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Designing a robust speech-recognition algorithm is a complex task requiring detailed knowledge of signal processing and statistical modeling.


Classifying Speech-Recognition Systems Most speech-recognition systems are classified as isolated or continuous. Isolated word recognition requires a brief pause between each spoken isolated word recognition, whereas continuous speech recognition does not.

Speech-recognition systems can be further classified as speaker-dependent or speaker-independent.


A speaker-dependent system only recognizes speech from one particular speaker's voice, whereas a speaker-independent system can recognize speech from anybody. The Development Workflow There are two major stages within isolated word recognition: In the testing stage we use acoustic models of these digits to recognize isolated words using a classification isolated word recognition.

The development workflow consists of three steps: Speech acquisition User interface development Acquiring Speech For training, speech is acquired from a microphone and brought into the development environment for offline analysis. For testing, speech is continuously streamed into the environment for online isolated word recognition.

During the training stage, it is necessary to record repeated utterances of each digit in the dictionary.

Developing an Isolated Word Recognition System in MATLAB - MATLAB & Simulink

This approach works well for training data. In the testing stage, however, we need to continuously acquire and buffer speech samples, and at the same time, process the incoming speech frame by frame, isolated word recognition in continuous groups of samples.

Data is acquired and processed in frames of 80 samples. We then derive an acoustic model that gives a robust representation of each word at isolated word recognition training stage.

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Finally, we select an appropriate classification algorithm for the testing stage. Developing a Speech-Detection Algorithm The speech-detection algorithm is developed by processing the prerecorded speech frame by frame within a simple loop. Signal energy works well for detecting voiced signals, while zero-crossing counts work well for detecting unvoiced signals.

To avoid identifying ambient isolated word recognition as speech, we assume that each isolated word will last at least 25 milliseconds. Developing the Acoustic Model A good acoustic model should be derived from speech characteristics that will enable the system to distinguish isolated word recognition the different words in the dictionary.

We know that different sounds are produced by varying the isolated word recognition of the human vocal tract and that these different sounds can have different frequencies.

Developing an Isolated Word Recognition System in MATLAB

To investigate these frequency characteristics we examine the power spectral density PSD estimates of various spoken digits. We select an arbitrary value of 12, which is typical in speech applications. We can see that the peaks in the PSD remain consistent for a isolated word recognition digit but differ between digits.


This means that we can derive the acoustic models in our system from spectral features. Isolated word recognition the linear predictive filter coefficients, we can obtain several feature vectors using Signal Processing Toolbox functions, including reflection coefficients, log area ratio parameters, and line spectral frequencies.

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