# US 9,520,123 B2 · System and method for pruning redundant units in a speech synthesis process

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- Number: US 9,520,123 B2
- Document ID: US9520123B2
- Status: Granted
- Kind: B2
- Application number: 14/662,872
- Filed: 2015-03-19
- Published: 2016-09-22
- Granted: 2016-12-13
- Earliest claimed: 2015-03-19
- Assignees: Nuance Communications, Inc.
- Applicants: Nuance Communications, Inc.
- Inventors: Heng Lu; Xu Shao; Wei Zhang; Wenhui Lei; Andrew Breen
- Web page: https://patentgrep.com/patent/US9520123B2

## CPC

- G10L 13/00: Speech synthesis; Text to speech systems
- G10L 13/027: Concept to speech synthesisers; Generation of natural phrases from machine-based concepts (generation of parameters for speech synthesis out of text G10L13/08)
- G10L 13/047: Architecture of speech synthesisers
- G10L: SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING

## Abstract

A system and method for concatenative speech synthesis is provided. Embodiments may include accessing, using one or more computing devices, a plurality of speech synthesis units from a speech database and determining a similarity between the plurality of speech synthesis units. Embodiments may further include retrieving two or more speech synthesis units having the similarity and pruning at least one of the two or more speech synthesis units based upon, at least in part, the similarity.

## Claims

### Claim 1

A computer-implemented method for concatenative speech synthesis comprising:
accessing, using one or more computing devices, a plurality of speech synthesis units from a speech database;
determining a similarity between the plurality of speech synthesis units, wherein determining the similarity is based upon, at least in part, a similarity factor;
retrieving two or more speech synthesis units having the similarity; and
pruning at least one of the two or more speech synthesis units based upon, at least in part, the similarity, wherein pruning is based upon, at least in part, a delta unit appearance frequency “DUAF” technique and wherein the DUAF is based upon, at least in part, a change in a unit appearance frequency and a change of the similarity factor.

### Claim 2 (depends on 1)

The method of claim 1, wherein the pruning is based upon, at least in part, a delta unit appearance frequency technique and a unit appearance frequency technique.

### Claim 3 (depends on 1)

The method of claim 1, further comprising:
receiving a text input corresponding to an utterance during a massive synthesis phase associated with the concatenative speech synthesis.

### Claim 4 (depends on 3)

The method of claim 3, further comprising:
determining a unit set based upon, at least in part, the utterance.

### Claim 5 (depends on 4)

The method of claim 4, further comprising:
providing the unit set as feedback prior to the pruning.

### Claim 6

A non-transitory computer-readable storage medium having stored thereon instructions, which when executed by a processor result in one or more concatenative speech synthesis operations, the operations comprising:
accessing, using one or more computing devices, a plurality of speech synthesis units from a speech database;
determining a similarity between the plurality of speech synthesis units, wherein determining the similarity is based upon, at least in part, a similarity factor;
retrieving two or more speech synthesis units having the similarity; and
pruning at least one of the two or more speech synthesis units based upon, at least in part, the similarity, wherein pruning is based upon, at least in part, a delta unit appearance frequency “DUAF” technique and wherein the DUAF is based upon, at least in part, a change in a unit appearance frequency and a change of the similarity factor.

### Claim 7 (depends on 6)

The non-transitory computer-readable storage medium of claim 6, wherein the pruning is based upon, at least in part, a delta unit appearance frequency technique and a unit appearance frequency technique.

### Claim 8 (depends on 6)

The non-transitory computer-readable storage medium of claim 6, further comprising:
receiving a text input corresponding to an utterance during a massive synthesis phase associated with the concatenative speech synthesis.

### Claim 9 (depends on 8)

The non-transitory computer-readable storage medium of claim 8, further comprising:
determining a unit set based upon, at least in part, the utterance.

### Claim 10 (depends on 9)

The non-transitory computer-readable storage medium of claim 9, further comprising:
providing the unit set as feedback prior to the pruning.

### Claim 11

A system configured to perform concatenative speech synthesis comprising:
one or more processors configured to access a plurality of speech synthesis units from a speech database and determine a similarity between the plurality of speech synthesis units, wherein determining the similarity is based upon, at least in part, a similarity factor, the one or more processors further configured to retrieve two or more speech synthesis units having the similarity, the one or more processors further configured to prune at least one of the two or more speech synthesis units based upon, at least in part, the similarity, wherein pruning is based upon, at least in part, a delta unit appearance frequency “DUAF” technique and wherein the DUAF is based upon, at least in part, a change in a unit appearance frequency and a change of the similarity factor.

### Claim 12 (depends on 11)

The system of claim 11, wherein the pruning is based upon, at least in part, a delta unit appearance frequency technique and a unit appearance frequency technique.

### Claim 13 (depends on 11)

The system of claim 11, further comprising:
receiving a text input corresponding to an utterance during a massive synthesis phase associated with the concatenative speech synthesis.

### Claim 14 (depends on 13)

The system of claim 13, further comprising:
determining a unit set based upon, at least in part, the utterance; and
providing the unit set as feedback prior to the pruning.