Cypress Semiconductor Corporation

Spansion Llc.

Ojas Ashok Bapat

Richard M Fastow

Jens Olson

Kenichi Kumatani

Chen Liu

A method, apparatus, and tangible computer readable medium for processing a Hidden Markov Model (HMM) structure are disclosed herein. For example, the method includes receiving Hidden Markov Model (HMM) information from an external system. The method also includes processing back pointer data and first HMM states scores for one or more NULL states in the HMM information. Second HMM state scores are processed for one or more non-NULL states in the HMM information based on at least one predecessor state. Further, the method includes transferring the second HMM state scores to the external system.

Hidden Markov Model processing engine

Embodiments include a method, apparatus, and computer program product for authentication for speech recognition. The method can include sensing an authentication device with a target device. One or more decoded voice commands can be processed after verification of the authentication device by the target device. Further, one or more decoded voice commands can be executed by the target device.

Authentication for recognition systems

Embodiments include a method, apparatus, and a computer program product for combining results from multiple decoders. For example, the method can include generating a network of paths based on one or more outputs associated with each of the multiple decoders. The network of paths can be scored to find an initial path with the highest path score based on scores associated with the one or more outputs. A weighting factor can be calculated for each of the multiple decoders based on a number of outputs from each of the multiple decoders included in the initial path with the highest path score. Further, the network of paths can be re-scored to find a new path with the highest path score based on the scores associated with the one or more outputs and the weighting factor for each of the multiple decoders.

Combining of results from multiple decoders

Embodiments of the present invention include an acoustic processing device and a method for traversing a Hidden Markov Model (HMM). The acoustic processing device can include a senone scoring unit (SSU), a memory device, a HMM module, and an interface module. The SSU is configured to receive feature vectors from an external computing device and to calculate senones. The memory device is configured to store the senone scores and HMM information, where the HMM information includes HMM IDs and HMM state scores. The HMM module is configured to traverse the HMM based on the senone scores and the HMM information. Further, the interface module is configured to transfer one or more HMM scoring requests from the external computing device to the HMM module and to transfer the HMM state scores to the external computing device.

Phoneme score accelerator

A method, system and tangible computer readable medium for generating one or more wake-up words are provided. For example, the method can include receiving a text representation of the one or more wake-up words. A strength of the text representation of the one or more wake-up words can be determined based on one or more static measures. The method can also include receiving an audio representation of the one or more wake-up words. A strength of the audio representation of the one or more wake-up words can be determined based on one or more dynamic measures. Feedback on the one or more wake-up words is provided (e.g., to an end user) based on the strengths of the text and audio representations.

Generation of wake-up words

Embodiments of the present invention include a data storage device and a method for storing data in a hash table. The data storage device can include a first memory device, a second memory device, and a processing device. The first memory device is configured to store one or more data elements. The second memory device is configured to store one or more status bits at one or more respective table indices. In addition, each of the table indices is mapped to a corresponding table index in the first memory device. The processing device is configured to calculate one or more hash values based on the one or more data elements.

Hybrid hashing scheme for active HMMS

Embodiments of the present invention include an acoustic processing device, a method for acoustic signal processing, and a speech recognition system. The speech processing device can include a processing unit, a histogram pruning unit, and a pre-pruning unit. The processing unit is configured to calculate one or more Hidden Markov Model (HMM) pruning thresholds. The histogram pruning unit is configured to prune one or more HMM states to generate one or more active HMM states. The pruning is based on the one or more pruning thresholds. The pre-pruning unit is configured to prune the one or more active HMM states based on an adjustable pre-pruning threshold. Further, the adjustable pre-pruning threshold is based on the one or more pruning thresholds.

Histogram based pre-pruning scheme for active HMMS

Embodiments of the present invention include an apparatus, method, and system for calculating senone scores for multiple concurrent input speech streams. The method can include the following: receiving one or more feature vectors from one or more input streams; accessing the acoustic model one senone at a time; and calculating separate senone scores corresponding to each incoming feature vector. The calculation uses a single read access to the acoustic model for a single senone and calculates a set of separate senone scores for the one or more feature vectors, before proceeding to the next senone in the acoustic model.

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Sunnyvale, CA, United States of America

Ojas Ashok Bapat