Age Classification based on Machine Learning

학위논문 (석사)-- 서울대학교 대학원 : 인문대학 언어학과, 2018. 8. 정민화.본 연구는 대검찰청에서 수집한 한국인 대규모 음성 코퍼스를 사용하여 기계 학습 모델을 통해 연령을 분류하는 것을 목적으로 한다. 학습 모델은 20대, 30~40대, 50대 이상으로 3분류를 한다. 실험을 위해 묵음(silence)을 기준으로 음성 코퍼스를 378,684개의 데이터로 분절하였으며, 발화 유형과 성별로 데이터를 구분하였다. 음성으로부터 Mel Frequency Cepstral Coefficients(MFCCs), fundamental frequency(F0), i-vector, jitter, shimmer, 발화속도를 추출하여 기계 학습 모델인 Long Short Term Memory(LSTM) 모델을 통해 연령을 분류하였다. 또한, feature selection 알고리즘을 통해 각 음성 특징의 영향을 확인하여 특징마다 가중치를 달리한 실험도 진행하였다. 실험에서는 음성 특징별 성능과 음성 특징의 조합의 성능으로 나누어 험하였다. 그 결과, 개별 음성 특징의 경우 MFCC로 학습하였을 때 76.01%로 가장 높았으며, 음성 특징의 조합의 경우 모든 음성 특징을 학습하였을 때 80.01%로 가장 높았다. 또한, Recursive Feature Elimination (RFE)나 Extra Tree Classifier (ETC)와 같은 feature selection 알고리즘을 적용하였을 때는 80.87%로 본 연구에서 가장 높은 성능을 보였다.1. 서론 1 2. 선행 연구 3 3. 연구 방법 8 3.1 음성 코퍼스 8 3.1.1 음성 코퍼스의 구성 8 3.1.2 데이터 분절 12 3.2 실험 모델 15 3.3 음성 특징 추출 17 3.3.1 Mel Frequency Cepstral Coefficients (MFCCs) 17 3.3.2 i-vector 21 3.3.3 Fundamental frequency (F0) 24 3.3.4 Jitter 25 3.3.5 Shimmer 27 3.3.6 발화속도 29 4. 실험 31 4.1 실험 설계 31 4.2 실험 결과 33 4.2.1 음성 특징별 성능 34 4.2.2 조합 성능 35 4.2.3 feature selection 적용 후 성능 36 4.3 토의 39 5. 결론 43 참고 문헌 44 부록 48 음성 특징별 혼동 행렬 48 음성 코퍼스 발화 유형 52 Abstract 59Maste

A Framework For Enhancing Speaker Age And Gender Classification By Using A New Feature Set And Deep Neural Network Architectures

Speaker age and gender classification is one of the most challenging problems in speech processing. Recently with developing technologies, identifying a speaker age and gender has become a necessity for speaker verification and identification systems such as identifying suspects in criminal cases, improving human-machine interaction, and adapting music for awaiting people queue. Although many studies have been carried out focusing on feature extraction and classifier design for improvement, classification accuracies are still not satisfactory. The key issue in identifying speaker’s age and gender is to generate robust features and to design an in-depth classifier. Age and gender information is concealed in speaker’s speech, which is liable for many factors such as, background noise, speech contents, and phonetic divergences. In this work, different methods are proposed to enhance the speaker age and gender classification based on the deep neural networks (DNNs) as a feature extractor and classifier. First, a model for generating new features from a DNN is proposed. The proposed method uses the Hidden Markov Model toolkit (HTK) tool to find tied-state triphones for all utterances, which are used as labels for the output layer in the DNN. The DNN with a bottleneck layer is trained in an unsupervised manner for calculating the initial weights between layers, then it is trained and tuned in a supervised manner to generate transformed mel-frequency cepstral coefficients (T-MFCCs). Second, the shared class labels method is introduced among misclassified classes to regularize the weights in DNN. Third, DNN-based speakers models using the SDC feature set is proposed. The speakers-aware model can capture the characteristics of the speaker age and gender more effectively than a model that represents a group of speakers. In addition, AGender-Tune system is proposed to classify the speaker age and gender by jointly fine-tuning two DNN models; the first model is pre-trained to classify the speaker age, and second model is pre-trained to classify the speaker gender. Moreover, the new T-MFCCs feature set is used as the input of a fusion model of two systems. The first system is the DNN-based class model and the second system is the DNN-based speaker model. Utilizing the T-MFCCs as input and fusing the final score with the score of a DNN-based class model enhanced the classification accuracies. Finally, the DNN-based speaker models are embedded into an AGender-Tune system to exploit the advantages of each method for a better speaker age and gender classification. The experimental results on a public challenging database showed the effectiveness of the proposed methods for enhancing the speaker age and gender classification and achieved the state of the art on this database