Speech recognition method and device

Abstract

The invention relates to the speech technology and discloses a speech recognition method and device. The method comprises the following steps: carrying out soft clustering calculation in advance according to N Gausses obtained through model training to obtain M soft clustering Gausses; during speech recognition, carrying out speech conversion to obtain feature vectors, and carrying out calculation according to the feature vectors to obtain the front L soft clustering Gausses, the scores of which are highest, wherein the L is smaller than M; and carrying out acoustic model likelihood calculating with each member Gauss in the L soft clustering Gausses serving as the Gauss needing to participate in calculation in an acoustic model in the speech recognition process. The method adopts a dynamic Gauss selection mode during speech recognition, thereby reducing the number of the Gausses needing to be evaluated in the acoustic model in the speech recognition process, and improving speed and accuracy of likelihood evaluation of the acoustic model.

Description

technical field [0001] The invention relates to speech technology, in particular to a speech recognition method. Background technique [0002] With the development of speech recognition technology, the accuracy of speech recognition technology has made great progress in recent years with the promotion of deep learning, especially in cloud-based services. Most of the existing speech recognition services are implemented in the cloud, and the speech needs to be uploaded to the server, and the server performs acoustic evaluation on the uploaded speech to give the recognition result. In order to improve the recognition rate, most servers use deep learning methods to evaluate speech. However, deep learning requires huge computing resources and is not suitable for local or embedded devices. And in many usage scenarios that cannot be connected to the Internet, they can only rely on local speech recognition technology. Due to limited local computing and storage resources, Hidden M...

AI Technical Summary

Problems solved by technology

Clustering accuracy is low

[0007] 2. When clustering, the mean and variance of the member Gaussians are directly used as the input of the clustering. When training the clustering Gaussians, the mean and variance are directly made a simple arithmetic mean, and the clustering accuracy is extremely low.

[0008] 3. When clustering, there is no effective iterative method, causing the cluster to converge to a local optimum

[0009] 4. The Gaussian selection during recognition cannot be dynamically updated, resulting in too many member Gaussians remaining in the calculation, and the recognition speed is slow

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