Method and apparatus for detecting speech endpoint using weighted finite state transducer

A method for performing speech recognition by detecting a speech endpoint, the method comprising:

• receiving a speech signal obtained through a speech input device of a voice-operated user interface;
• storing the received speech signal in a storage;
• receiving frame units of a feature vector converted from the speech signal stored in the storage;
• analyzing and classifying the feature vector into a speech class and a noise class;
• creating a frame level weighted finite state transducer (WFST) by converting the speech class and the noise class to a WFST format after receiving the speech class and the noise class;
• creating a speech level WFST detecting a speech endpoint by analyzing a relationship between the speech class and noise class and a preset state;
• obtaining a combined WFST by combining the frame level WFST with the speech level WFST;
• optimizing the combined WFST;
• and
• performing speech recognition based on the detected speech endpoint of the speech signal,
• wherein the analyzing and classifying the feature vector, creating the frame level WFST, creating the speech level WFST, obtaining the combined WFST, optimizing the combined WFST, and performing speech recognition are performed using at least one specially programmed processor of a special purpose computer.

A non-transitory recording media having one or more computer-readable programs written therein, that when executed performs a method for performing speech recognition by detecting a speech endpoint, the method comprising:

• receiving a speech signal obtained through a speech input device of a voice-operated user interface;
• storing the received speech signal in a storage;
• receiving frame units of a feature vector converted from the speech signal stored in the storage;
• analyzing and classifying the feature vector into a speech class and a noise class;
• creating a frame level weighted finite state transducer (WFST) by converting the speech class and the noise class to a WFST format after receiving the speech class and the noise class;
• creating a speech level WFST detecting a speech endpoint by analyzing a relationship between the speech class and noise class and a preset state;
• obtaining a combined WFST by combining the frame level WFST with the speech level WFST;
• optimizing the combined WFST;
• and
• performing speech recognition based on the detected speech endpoint of the speech signal,
• wherein the analyzing and classifying the feature vector, creating the frame level WFST, creating the speech level WFST, obtaining the combined WFST, and optimizing the combined WFST, and performing speech recognition are performed using at least one specially programmed processor of a special purpose computer.