A speech recognition system

Abstract

This invention discloses a phonetic identifying system including a modulus conversion unit, a character pick up unit, a decoding operating unit and an acoustic model for receiving phonetic input signal and gets matched identified result, among which, said modulus conversion unit converts the phonetic input signal into digit signal, said character pick up unit frames said digital signal to pick up phonetic character parameters to get input phonetic character vector sequence. Said decoding operation unit carries out decoding operation to said vector sequence to get the identified result.

Description

technical field [0001] The invention relates to a speech recognition system. Background technique [0002] Almost all current speech recognition systems use methods based on statistical pattern recognition. In all speech recognition systems, it is necessary to convert the time-domain sound waves of speech input into a digital vector feature to describe and distinguish different pronunciations, which we call Speech features, based on which a sound model is established for all pronunciations, which is usually called an acoustic model in the field of speech recognition. All speech recognition systems must have an acoustic model; at the same time, for large vocabulary continuous speech recognition systems, a language model is also required. The purpose of speech recognition is to give a string of sound feature sequences as input conditions, use acoustic models and language models, and use search algorithms to output recognition results, such as words, words or sentences. In th...

AI Technical Summary

Problems solved by technology

However, if CDHMM is used in a speech recognition system with a large vocabulary, the Gaussian probability needs to be calculated multiple times when the decoding operation unit performs decoding. Usually, the amount of calculation required in the decoding process is concentrated on the calculation of the Gaussian probability, which requires a lot of Calculations

When large-vocabulary speech recognition is performed on embedded hardware platforms with limited resources such as mobile phones, the response speed of the speech recognition system will be very slow, which cannot meet the needs of actual use.

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