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Phoneme Based Embedded Segmental K-Means for Unsupervised Term Discovery

Identifying and grouping the frequently occurring word-like patterns from raw acoustic waveforms is an important task in the zero resource speech processing. Embedded segmental K-means (ES-KMeans) discovers both the word boundaries and the word types from raw data. Starting from an initial set of subword boundaries, the ES-Kmeans iteratively eliminates some of the boundaries to arrive at frequently occurring longer word patterns. Notice that the initial word boundaries will not be adjusted during the process. As a result, the performance of the ES-Kmeans critically depends on the initial subword boundaries. Originally, syllable boundaries were used to initialize ES-Kmeans. In this paper, we propose to use a phoneme segmentation method that produces boundaries closer to true boundaries for ES-KMeans initialization. The use of shorter units increases the number of initial boundaries which leads to a significant increment in the computational complexity. To reduce the computational cost, we extract compact lower dimensional embeddings from an auto-encoder. The proposed algorithm is benchmarked on Zero Resource 2017 challenge, which consists of 70 hours of unlabeled data across three languages, viz. English, French, and Mandarin. The proposed algorithm outperforms the baseline system without any language-specific parameter tuning.