Method and mobile device for awareness of language ability
First Claim
1. A method for awareness of language ability in a mobile device, comprising:
- an audio processing step, wherein after a voice is received by a voice collection element, a voice activity detection module in a language ability evaluation unit extracts a voice segment with speech sound from the voice, and a feature extraction module in the language ability evaluation unit calculates a feature vector sequence of the voice segment, that is, extracts a voice segment feature vector for analysis;
a repeated pattern index estimating step, wherein a steady state voice segment detection and quantization module in the language ability evaluation unit directly obtains a codeword sequence, a repeated voice segment detection module in the language ability evaluation unit performs a repeated voice segment matching algorithm, so as to determine whether the codeword sequence contains one or at least one repeated voice segment, and not only a full-domain language model is established based on codewords of common daily expressions, but also a catching language model is established based on codewords that occur recently, which are used in repeated voice segment matching, so as to obtain a repeated pattern index; and
a community interaction index estimating step, wherein a speaker diarization module in the language ability evaluation unit detects a speaking time/times ratio of speakers, a conversation time length, and a speaker alternation times, and even detects whether a phenomenon of soliloquy exists, so as to obtain a community interaction index;
wherein steps of the repeated voice segment matching algorithm comprises;
codeword encoding for homogeneous voice segments and a codeword language model, and in the step of codeword encoding for homogeneous voice segments, voice segment division and codeword encoding are directly performed for several homogeneous voice segments on a time axis, a state number of a Semi-Hidden Markov Model (Semi-HMM) is set as 1, length features of the homogeneous voice segments are described by using a duration model, and properties of the homogeneous voice segment length are maintained through the duration model.
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Abstract
A method and mobile device for awareness of language ability are provided. “Repeated pattern index”-related properties, such as, a vocabulary usage amount, a vocabulary type, or a ratio, a time point, a time length or repeated contents of a repeated voice segment, and “community interaction index”-related properties, such as, a number of persons who speak with a user, a conversation time length, or whether the user talks alone during each time interval, are extracted according to voice data collected by a voice collection element worn on the user. Then, a language ability of the user is further calculated, so as to provide evaluation of the language ability of a dementia patient for reference.
67 Citations
7 Claims
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1. A method for awareness of language ability in a mobile device, comprising:
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an audio processing step, wherein after a voice is received by a voice collection element, a voice activity detection module in a language ability evaluation unit extracts a voice segment with speech sound from the voice, and a feature extraction module in the language ability evaluation unit calculates a feature vector sequence of the voice segment, that is, extracts a voice segment feature vector for analysis; a repeated pattern index estimating step, wherein a steady state voice segment detection and quantization module in the language ability evaluation unit directly obtains a codeword sequence, a repeated voice segment detection module in the language ability evaluation unit performs a repeated voice segment matching algorithm, so as to determine whether the codeword sequence contains one or at least one repeated voice segment, and not only a full-domain language model is established based on codewords of common daily expressions, but also a catching language model is established based on codewords that occur recently, which are used in repeated voice segment matching, so as to obtain a repeated pattern index; and a community interaction index estimating step, wherein a speaker diarization module in the language ability evaluation unit detects a speaking time/times ratio of speakers, a conversation time length, and a speaker alternation times, and even detects whether a phenomenon of soliloquy exists, so as to obtain a community interaction index; wherein steps of the repeated voice segment matching algorithm comprises;
codeword encoding for homogeneous voice segments and a codeword language model, and in the step of codeword encoding for homogeneous voice segments, voice segment division and codeword encoding are directly performed for several homogeneous voice segments on a time axis, a state number of a Semi-Hidden Markov Model (Semi-HMM) is set as 1, length features of the homogeneous voice segments are described by using a duration model, and properties of the homogeneous voice segment length are maintained through the duration model. - View Dependent Claims (2, 3, 4)
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5. A mobile device for awareness of language ability, comprising:
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an analysis platform; a voice collection element, electrically connected to the analysis platform, for collecting required voice data; and a language ability evaluation unit, embedded in the analysis platform, or electrically connected to the analysis platform, wherein the language ability evaluation unit receives the voice data collected by the voice collection element, evaluates and calculates a language ability, and outputs an evaluation result; wherein the evaluation result comprises a repeated pattern index and a community interaction index; wherein the repeated pattern index is evaluated and calculated according to one or more of the following properties;
a vocabulary usage amount, a vocabulary type, and a ratio, a time point, a time length and repeated contents of a repeated voice segment, and the community interaction index is evaluated and calculated according to each of the following properties;
a number of persons who speak with a user, a conversation time length, and whether the user talks alone during each time interval. - View Dependent Claims (6, 7)
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Specification