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.