Eiji Ohira

Speech detecting method

Speech signal presence is decided if total signal power is above a first threshold, and if either low or high frequency components exceed thresholds as a large fraction of the total power. Total power is calculated as the zero-order auto-correlation coefficient, and fractional power of frequency components is calculated as the first-order partial auto-correlation coefficient.

Method and apparatus for registering standard pattern for speech recognition

In order to produce and store a pattern of reference words for use in a speaker-dependent speech recognition system, the system prompts the operator of the system to speak standard words in a predetermined sequence. For this purpose, a prestored standard word is spoken by the system with a predetermined length, power and rhythm, and the operator then repeats the standard words while attempting to simulate the same predetermined length, power and rhythm. The standard word repeated by the operator is detected and processed to determine whether it meets a certain resemblance criteria with respect to the standard word as spoken by the system. If the standard word repeated by the operator does not meet the resemblance criteria, the system repeats the same standard word to prompt the operator to try again; and, if the standard word repeated by the operator meets the resemblance criteria, it is stored as a reference word. This operation is repeated for each of the sequence of prestored standard words.

LSI implementation of a pattern matching algorithm for speech recognition

Many speech recognition systems contain the foUowing functional blocks: a voice input circuit, a feature extractor (analyzer), a unit for calculating the distance between input and standard patterns at every frame, a memory for storing standard patterns, a unit for matching whole word patterns (a pattern matching circuit), and a final decision and system control circuit. The pattern matching circuit is independent of the recognition algorithm and requires many conventional integrated circuits for implementation. Therefore, we decided to develop a custom integrated circuit for the pattern matching function. Due to its original architecture, our integrated circuit is able to execute in real time a continuous nonlinear matching process and is able to handle large volumes of data. This integrated circuit has been designed to be suitable for both isolated word recognition and connected speech recognition. The circuit has been tested in a pilot isolated-word and connected-speech recognition system. In this paper, the authors describe this integrated circuits specifications, architecture, and performance, as well as its application in the model system.