Microsoft Technology Licensing, LLC

Lucent Technologies Inc.

Frank Kao-ping Soong

Min Chu

Lei Ma

Yong Zhao

Yu Shi

Ye Tian

Qi P Li

Jian-Lai Zhou

Lijuan Wang

Yusheng Li

Xin Zou

Olivier Siohan

Described is a technology by which synthesized speech generated from text is evaluated against a prosody model (trained offline) to determine whether the speech will sound unnatural. If so, the speech is regenerated with modified data. The evaluation and regeneration may be iterative until deemed natural sounding. For example, text is built into a lattice that is then (e.g., Viterbi) searched to find a best path. The sections (e.g., units) of data on the path are evaluated via a prosody model. If the evaluation deems a section to correspond to unnatural prosody, that section is replaced, e.g., by modifying/pruning the lattice and re-performing the search. Replacement may be iterative until all sections pass the evaluation. Unnatural prosody detection may be biased such that during evaluation, unnatural prosody is falsely detected at a higher rate relative to a rate at which unnatural prosody is missed.

Unnatural prosody detection in speech synthesis

Described is a bimodal data input technology by which handwriting recognition results are combined with speech recognition results to improve overall recognition accuracy. Handwriting data and speech data corresponding to mathematical symbols are received and processed (including being recognized) into respective graphs. A fusion mechanism uses the speech graph to enhance the handwriting graph, e.g., to better distinguish between similar handwritten symbols that are often misrecognized. The graphs include nodes representing symbols, and arcs between the nodes representing probability scores. When arcs in the first and second graphs are determined to match one another, such as aligned in time and associated with corresponding symbols, the probability score in the second graph for that arc is used to adjust the matching probability score in the first graph. Normalization and smoothing may be performed to correspond the graphs to one another and to control the influence of one graph on the other.

Handwriting symbol recognition accuracy using speech input

Described is a voice persona service by which users convert text into speech waveforms, based on user-provided parameters and voice data from a service data store. The service may be remotely accessed, such as via the Internet. The user may provide text tagged with parameters, with the text sent to a text-to-speech engine along with base or custom voice data, and the resulting waveform morphed based on the tags. The user may also provide speech. Once created, a voice persona corresponding to the speech waveform may be persisted, exchanged, made public, shared and so forth. In one example, the voice persona service receives user input and parameters, and retrieves a base or custom voice that may be edited by the user via a morphing algorithm. The service outputs a waveform, such as a .wav file for embedding in a software program, and persists the voice persona corresponding to that waveform.

Voice persona service for embedding text-to-speech features into software programs

Input is received from at least two different input sources. Information from these sources are combined together to provide a result. In a particular example, input from one source corresponds to potential recognition candidates, and input from another source corresponds to other potential candidates. These candidates are combined together to select a result.

Combined input processing for a computing device

A method and apparatus for extracting speech features from a speech signal in which the linear frequency spectrum data, as generated, for example, by a conventional frequency transform, is first converted to logarithmic frequency spectrum data having frequency data distributed on a substantially logarithmic (rather than linear) frequency scale. Then, a plurality of digital auditory filters is applied to the resultant logarithmic frequency spectrum data, each of these filters having a substantially similar shape, but centered at different points on the logarithmic frequency scale. Because each of the filters have a similar shape, the feature extraction approach of the present invention advantageously can be easily modified or tuned by adjusting each of the filters in a coordinated manner, with the adjustment of only a handful of filter parameters.

Automatic speech recognition with psychoacoustically-based feature extraction, using easily-tunable single-shape filters along logarithmic-frequency axis

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Warren, NJ, United States of America

Frank Kao-ping Soong