Abstract: A method is provided which performs word recognition using the dynamic recurrent neural networks (DRNN) model and which is able to discriminate, with high precision, similar words for which misrecognition often occurs. When the vocal sounds of some words are input, the DRNN output corresponding to the input word vocal data is generated by the word detection signal output component using the DRNN word model and encoded into coded data by using a code book. When the DRNN output from the word detection signal output component has a correctness of a predetermined or greater level, a processor establishes a fixed period that includes the characteristic components of the input words in the DRNN output. The processor then examines the code data in the established fixed period. Discrimination of input words and words that are similar to the input words is accomplished on the basis of the examination results.

Abstract: Techniques for implementing adaptable voice activation operations for interactive speech recognition devices and instruments. Specifically, such speech recognition devices and instruments include an input sound signal power or volume detector in communication with a central CPU for bringing the CPU out of an initial sleep state upon detection of perceived voice exceeding a predetermined threshold volume level and is continuously perceived for at least a certain period of time. If both these conditions are satisfied, the CPU is transitioned into an active mode so that the perceived voice can be analyzed against a set of registered key words to determine if a "power on" command or similar instruction has been received. If so, the CPU maintains an active state in normal speech recognition processing ensues until a "power off" command is received.

Abstract: A technique for improving speech recognition in low-cost, speech interactive devices. This technique calls for selectively implementing a speaker-specific word enrollment and detection unit in parallel with a word detection unit to permit comprehension of spoken commands or messages when no recognizable words are found. Preferably, specific speaker detection will be based on the speaker's own personal list of words or expression. Other facets include complementing non-specific pre-registered word characteristic information with individual, speaker-specific verbal characteristics to improve recognition in cases where the speaker has unusual speech mannerisms or accent and response alteration in which speaker-specification registration functions are leveraged to provide access and permit changes to a predefined responses table according to user needs and tastes.

Abstract: A technique for improving voice recognition in low-cost, speech interactive devices. This technique calls for implementing a affirmative/negative discrimination unit in parallel with a word detection unit to permit comprehension of spoken commands or messages issued by binary questions when no recognizable words are found. Preferably, affirmative/negative discrimination will include either spoken vowel analysis or negative language descriptor detection of the perceived message or command. Other facets include keyword identification within the perceived message or command, confidence match level comparison or correlation table compilation in order to increase recognition accuracy of word-based recognition, volume analysis, and inclusion of ambient environment information in generating responses to perceived messages or queries.

Abstract: A technique for improving speech recognition in low-cost, speech interactive devices. This technique calls for selectively implementing a speaker-specific word enrollment and detection unit in parallel with a word detection unit to permit comprehension of spoken commands or messages when no recognizable words are found. Preferably, specific speaker detection will be based on the speaker's own personal list of words or expression. Other facets include complementing non-specific pre-registered word characteristic information with individual, speaker-specific verbal characteristics to improve recognition in cases where the speaker has unusual speech mannerisms or accent and response alteration in which speaker-specification registration functions are leveraged to provide access and permit changes to a predefined responses table according to user needs and tastes.

Abstract: A speech recognition apparatus using a neural network is provided. A neuron-like element stores a value of its inner conditions. The neuron-like element also updates a value of its internal status on the basis of an output from the neuron-like element itself, outputs from other neuron-like elements and an external input outside. The neuron-like element also converts a value of its internal status into an external output. Accordingly, the neuron-like element itself can retain the history of input data. This enables time series data, such as speech, to be processed without providing any special devices in the neural network.

Abstract: A technique for improving speech recognition in low-cost, speech interactive devices. This technique calls for implementing a speaker-specific word enrollment and detection unit in parallel with a word detection unit to permit comprehension of spoken commands or messages issued by binary questions when no recognizable words are found. Preferably, specific speaker detection will be based on the speaker's own personal list of words or expression. Other facets include complementing non-specific pre-registered word characteristic information with individual, speaker-specific verbal characteristics to improve recognition in cases where the speaker has unusual speech mannerisms or accent and response alteration in which speaker-specification registration functions are leveraged to provide access and permit changes to a predefined responses table according to user needs and tastes.

Abstract: A speech recognition apparatus using a neural network. A neuron-like element according to the present invention has a means for storing a value of the inner condition thereof, a means for updating a value of internal status on the basis of an output from the neuron-like element itself, outputs from other neuron-like elements and an external input, and an output value generating means for converting a value of internal status into an external output. Accordingly, the neuron-like element itself can retain the history of input data. This enables the time series data, such as speech to be processed without providing any special means in the neural network.

Abstract: A speech recognition system can recognize a plurality of voice data having different patterns. The speech recognition system has a voice recognizing and processing device including a plurality of speech recognition neural networks that have previously learned different voice patterns to recognize given voice data. Each of the speech recognition neutral networks is adapted to judge whether or not input voice data coincides with one of the voice data to be recognized. Each neural network then outputs adaptation judgment data representing the adaptation in speech recognition. A selector responsive to the adaptation judgment data from each of the speech recognition neural networks selects one of the neural networks that has the highest adaptation in speech recognition. An output control device outputs the result of speech recognition from the speech recognition neural network selected by the selector.

Abstract: The speech recognition apparatus recognizes a frequency of successively input identical speech data sequences. The speech recognition apparatus includes a speech recognition non-layered neural network unit. Speech data sequence is inputted as feature vectors from a feature extracting unit. The neural network performs speech recognition and determines whether the input speech data sequence matches at least one predetermined speech data sequence. The neural network generates a speech recognition signal when the input speech data sequence matches the at least one predetermined speech data sequence. A recognition signal detecting unit outputs a reset instruction signal each time the neural network generates the speech recognition signal. An internal state value setting unit resets the neural network unit to an initial state each time the recognition signal detecting unit outputs the reset instruction signal.