Ojas Ashok Bapat

US Patent:

20140195233, Jul 10, 2014

Filed:

Jan 8, 2013

Appl. No.:

13/736618

Inventors:

- Sunnyvale CA, US
Ojas Ashok BAPAT - Sunnyvale CA, US

Assignee:

Spansion LLC - Sunnyvale CA

International Classification:

G10L 15/00

US Classification:

704246

Abstract:

Embodiments of the present invention include an apparatus, method, and system for speech recognition of a voice command. The method can include receiving data representing a voice command, generating a list of targets based on the state information of each target within the system, and selecting a target from the list of targets, based on the voice command.

US Patent:

20140180690, Jun 26, 2014

Filed:

Dec 21, 2012

Appl. No.:

13/725173

Inventors:

- Sunnyvale CA, US
Ojas A. Bapat - Sunnyvale CA, US

Assignee:

Spansion LLC - Sunnyvale CA

International Classification:

G10L 15/28

US Classification:

704251

Abstract:

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.

US Patent:

20140180693, Jun 26, 2014

Filed:

Dec 21, 2012

Appl. No.:

13/725224

Inventors:

- Sunnyvale CA, US
Ojas Ashok BAPAT - Sunnyvale CA, US

Assignee:

Spansion LLC - Sunnyvale CA

International Classification:

G10L 15/02

US Classification:

7042561

Abstract:

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.

US Patent:

20140180694, Jun 26, 2014

Filed:

Dec 21, 2012

Appl. No.:

13/725260

Inventors:

- Sunnyvale CA, US
Ojas A. Bapat - Sunnyvale CA, US
Jens Olson - Saratoga CA, US

Assignee:

Spansion LLC - Sunnyvale CA

International Classification:

G10L 15/02

US Classification:

7042561

Abstract:

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.

US Patent:

20140129217, May 8, 2014

Filed:

Nov 6, 2012

Appl. No.:

13/669907

Inventors:

- Sunnyvale CA, US
Ojas A. BAPAT - Sunnyvale CA, US

International Classification:

G10L 15/00

US Classification:

704231, 704E15001

Abstract:

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.