Abstract: A language identification and verification system is described whereby language identification is determined by finding the closest match of a speech utterance to multiple speaker sets. The language identification and verification system is implemented through use of a speaker identification/verification system as a baseline to find a set of well matched speakers in each of a plurality of languages. A comparison of unknown speech to speech features from such well-matched speakers is then made and a language decision is arrived on based on a closest match between the unknown speech features and speech features for such well matched reference speakers in a particular language.

Abstract: A speech coder apparatus operates to compress speech signals to a low bit rate. The apparatus includes a continuous speech recognizer (CSR) which has a memory for storing templates. Input speech is processed by the CSR where information in the speech is compared against the templates to provide an output digital signal indicative of recognized words, which signal is transmitted along a first path. There is further included a front end processor which is also responsive to the input speech signal for providing output digitized speech samples during a given frame interval. A side information encoder circuit responds to the output from the front end processor to provide at the output of the encoder a parameter signal indicative of the value of the pitch and word duration for each word as recognized by the CSR unit. The output of the encoder is transmitted as a second signal.

Abstract: A method and apparatus for recognizing an unknown speaker from a plurality of speaker candidates. Portions of speech from the speaker candidates and from the unknown speaker are sampled and digitized. The digitized samples are converted into frames of speech, each frame representing a point in an LPC-12 multi-dimensional speech space. Using a character covering algorithm, a set of frames of speech is selected, called characters, from the frames of speech of all speaker candidates. The speaker candidates' portions of speech are divided into smaller portions called segments. A smaller plurality of model characters for each speaker candidate is selected from the character set. For each set of model characters the distance from each speaker candidate's frame of speech to the closest character in the model set is determined and stored in a model histogram. When a model histogram is completed for a segment a distance D is found whereby at least a majority of frames have distances greater D.