Method and system for the automatic generation of speech features for scoring high entropy speech

• US 7,392,187 B2
• Filed: 09/20/2004
• Issued: 06/24/2008
• Est. Priority Date: 09/20/2004
• Status: Active Grant

• Current Assignee: Educational Testing Service
• Original Assignee: Educational Testing Service
• Inventors: Zechner, Klaus, Bejar, Isaac
• Primary Examiner(s): Hudspeth; David
• Assistant Examiner(s): HERNANDEZ, JOSIAH J
• Application Number:
  US10/945,150
• Publication Number:
  US 20060074655A1
• Time in Patent Office: 1,373 Days
• Field of Search: 704/243, 704/723, 434/156
• US Class Current: 704/243
• CPC Class Codes: G10L 15/063 Training

Abstract

A method and system for automatically generating a scoring model for scoring a speech sample are disclosed. One or more training speech samples are received in response to a prompt. One or more speech features are determined for each of the training speech samples. A scoring model is then generated based on the speech features. At least one of the training speech samples may be a high entropy speech sample. An evaluation speech sample is received and a score is assigned to the evaluation speech sample using the scoring model. The evaluation speech sample may be a high entropy speech sample.

Claims

1. A method of automatically generating a scoring model for scoring a speech sample, the method comprising:

receiving one or more training samples in response to a prompt;

determining one or more speech features for each of the training speech samples, wherein the one or more speech features comprise one or more of the following for each training speech sample;

a previously assigned score,

a lexical count,

a fluency measure,

a rate of speech measure,

a lexical similarity measure comprising one or more of an inner product of word frequencies for the training speech sample and a content vector, wherein the content vector comprises raw frequencies of word forms based on a corpus related to the prompt, and a ratio equal to the inner product divided by a number of words in the training speech sample, and

a speech sample utterance duration; and

generating a scorn model based on the speech features, wherein the scoring model is effective for scoring high entropy evaluation speech responses.

2. A method for scoring a high entropy speech sample, the method comprising:

retrieving a scoring model based on one or more speech features, wherein the one or more speech features comprise one or more of the following for each high entropy speech sample;

a previously assigned score,

a lexical count,

a fluency measure,

a rate of speech measure,

a lexical similarity measure comprising one or more of

an inner product of word frequencies for the high entropy speech sample and a content vector, wherein the content vector comprises raw frequencies of word forms based on a corpus related to the prompt, and

a ratio equal to the inner product divided by a number of words in the high entropy speech sample, and

a speech sample utterance duration; and

assigning a score to a high entropy speech sample using the scoring model.