Abstract: A method of speech recognition, in accordance with the present invention includes the steps of grouping acoustics to form classes based on acoustic features, clustering training speakers by the classes to provide class-specific cluster systems, selecting from the cluster systems, a subset of cluster systems closest to adaptation data from a test speaker, transforming the subset of cluster systems to bring the subset of cluster systems closer to the test speaker based on the adaptation data to form adapted cluster systems and combining the adapted cluster systems to create a speaker adapted system for decoding speech from the test speaker. System and methods for building speech recognition systems as well as adapting speaker systems for class-specific speaker clusters are included.

Abstract: A speech coding apparatus and method uses a hierarchy of prototype sets to code an utterance while consuming fewer computing resources. The value of at least one feature of an utterance is measured during each of a series of successive time intervals to produce a series of feature vector signals representing the feature values. A plurality of level subsets of prototype vector signals is computed, wherein each prototype vector signal in a higher level subset is associated with at least one prototype vector signal in a lower level subset. Each level subset contains a plurality of prototype vector signals, with lower level subsets containing more prototypes than higher level subsets. The closeness of the feature value of the first feature vector signal is compared to the parameter values of prototype vector signals in the first level subset of prototype vector signals to obtain a ranked list of prototype match scores for the first feature vector signal and each prototype vector signal in the first level subset.

Abstract: A method and apparatus is disclosed that allows people to carry on unobtrusive phone conversations in business or other settings where it is either not possible or impolite to talk. In the system of FIG. 1, the telephone user one will listen in the same manner as with a regular telephone. However, he will not speak into the telephone microphone. User one instead employs a unit including a keyboard to enter the text corresponding to what he wants to say. The text is converted into a synthesized speech using TTS apparatus and a voice output is sent to the microphone of the phone apparatus. The telephone apparatus transmits the synthesized voice signal over a standard telephone line to a unit including a conventional telephone speaker 26 and telephone microphone. User two, the party using the telephone at the other end, listens to a synthesized voice, but user one listens to the actual voice of user two with the telephone speaker, unless user two is also using a system similar to that of user one.

Abstract: Methods and apparatus for performing a tree search based acoustic fast match in a speech recognition system for decoding a speech utterance, the tree having a tree root and tree nodes connected by tree branches, the tree nodes having phonetic models associated therewith, are provided.

Abstract: A continuous speech recognition system has the ability to correct errors in strings of words. The error correction method stores data in the system's internal state to update probability tables used in developing alternative lists for substitution in misrecognized text.

Abstract: An automatic segmenter for continuous text segments such text in a rapid, consistent and semantically accurate manner. Two statistical methods for segmentation of continuous text are used. The first method, called "forward-backward matching", is easy and fast but can produce occasional errors in long phrases. The second method, called "statistical stack search segmenter", utilizes statistical language models to generate more accurate segmentation output at an expense of two times more execution time than the "forward-backward matching" method. In some applications where speed is a major concern, "forward-backward matching" can be used, while in other applications where highly accurate output is desired, "statistical stack search segmenter" is ideal.

Abstract: A method and apparatus for acoustic signal processing of speech recognition, the method comprising the following components: 1) Decompose each syllable into two phonemes of comparable length and complexity, the first one being a preme, and the second one being a toneme; 2) Each toneme is assigned a tone value such as high, rising, low, falling, and untoned; 3) No tone value is assigned to premes; 4) Pitch is detected continuously and treated the same way as energy and cepstrals in a Hidden Markov Model to predict the tone of a toneme; 5) The tone of a syllable is defined as the tone of its component toneme.

Abstract: A method and apparatus for estimating the probability of phones, a-posteriori, in the context of not only the acoustic feature at that time, but also the acoustic features in the vicinity of the current time, and its use in cutting down the search-space in a speech recognition system. The method constructs and uses a decision tree, with the predictors of the decision tree being the vector-quantized acoustic feature vectors at the current time, and in the vicinity of the current time. The process starts with an enumeration of all (predictor, class) events in the training data at the root node, and successively partitions the data at a node according to the most informative split at that node. An iterative algorithm is used to design the binary partitioning. After the construction of the tree is completed, the probability distribution of the predicted class is stored at all of its terminal leaves.

Abstract: A method of automatically aligning a written transcript with speech in video and audio clips. The disclosed technique involves as a basic component an automatic speech recognizer. The automatic speech recognizer decodes speech (recorded on a tape) and produces a file with a decoded text. This decoded text is then matched with the original written transcript via identification of similar words or clusters of words. The results of this matching is an alignment of the speech with the original transcript. The method can be used (a) to create indexing of video clips, (b) for "teleprompting" (i.e. showing the next portion of text when someone is reading from a television screen), or (c) to enhance editing of a text that was dictated to a stenographer or recorded on a tape for its subsequent textual reproduction by a typist.