Abstract: Acoustic models for speech recognition are automatically generated utilizing trained acoustic models from a native language and a foreign language. A phoneme-to-phoneme mapping is utilized to enable the description of foreign language words with native language phonemes. The phoneme-to-phoneme mapping is used for training foreign language words, described by native language phonemes on foreign language speech material. A new phonetic lexicon is created containing foreign language words and native language words transcribed by native language phonemes. Robust native language acoustic models can be derived utilizing foreign language and native language training material. The mapping may be used for training a grapheme to phoneme transducer (i.e., foreign language to native language) to generate native language pronunciations for new foreign language words.

Abstract: A system and method enabling acoustic barge-in during a voice prompt in a communication system. An acoustic prompt model is trained to represent the system prompt using the specific speech signal of the prompt. The acoustic prompt model is utilized in a speech recognizer in parallel with the recognizer's active vocabulary words to suppress the echo of the prompt within the recognizer. The speech recognizer may also use a silence model and traditional garbage models such as noise models and out-of-vocabulary word models to reduce the likelihood that noises and out-of-vocabulary words in the user utterance will be mapped erroneously onto active vocabulary words.

Abstract: A method and arrangement for speech recognition wherein a volume distance is determined between recognized words and the pauses lying between them. When the volume distance of a word is lower than a predetermined threshold, the word is evaluated as being incorrectly recognized, such that errors caused by unwanted noises are avoided.

Abstract: Acoustic models for speech recognition are automatically generated utilizing trained acoustic models from a native language and a foreign language. A phoneme-to-phoneme mapping is utilized to enable the description of foreign language words with native language phonemes. The phoneme-to-phoneme mapping is used for training foreign language words, described by native language phonemes on foreign language speech material. A new phonetic lexicon is created containing foreign language words and native language words transcribed by native language phonemes. Robust native language acoustic models can be derived utilizing foreign language and native language training material. The mapping may be used for training a grapheme to phoneme transducer (i.e., foreign language to native language) to generate native language pronunciations for new foreign language words.

Abstract: A system and method enabling acoustic barge-in during a voice prompt in a communication system. An acoustic prompt model is trained to represent the system prompt using the specific speech signal of the prompt. The acoustic prompt model is utilized in a speech recognizer in parallel with the recognizer's active vocabulary words to suppress the echo of the prompt within the recognizer. The speech recognizer may also use a silence model and traditional garbage models such as noise models and out-of-vocabulary word models to reduce the likelihood that noises and out-of-vocabulary words in the user utterance will be mapped erroneously onto active vocabulary words.

Abstract: In the recognition of spoken language, phonemes of the language are modelled by hidden Markov models. A modified hidden Markov model includes a conditional probability of a feature vector dependent on chronologically preceding feature vectors and, optionally, additionally comprises a conditional probability of a respectively current status. A global search for recognizing a word sequence in the spoken language is implemented with the modified hidden Markov model.

Abstract: A keyword is recognized in spoken language by assuming a start of this keyword is at every sampling time. An attempt is then made to image this keyword onto a sequence of HMM statusses that represent the keyword. The best path in a presentation space is determined with the Viterbi algorithm; and a local confidence standard is employed instead of the emission probability used in the Viterbi algorithm. When a global confidence standard that is composed of local confidence standards downwardly crosses a lower barrier for the best Viterbi path, then the keyword is recognized; and the sampling time assumed as start of the keyword is confirmed.