BottomUp Unsupervised Word Discovery via Acoustic Units Unsupervised term discovery is the task of identifying and grouping reoccurring wordlike patterns from the untranscribed audio data. It facilitates unsupervised acoustic model training in zero resource setting where no or minimal transcribed speech is available. In this paper, we investigate twostep bottomup approaches for unsupervised discovery of wordlike units. The first step discovers phonelike acoustic units from data and the second step combines the basic acoustic blocks to identify wordlike units. We investigated Embedded Segmental Kmeans and Nested Hierarchical PitmanYor (PYR) model as bottomup strategies. ESKMeans iteratively selects boundaries from an initial set to arrive at the word boundaries. The final performance critically depends on the quality of the initial boundaries. We used a segmentation method that discovers boundaries much closer to actual boundaries. PYR model has been used for word segmentation from space removed text data, and here we use it for word discovery from unsupervised acoustic units. The term discovery performance is evaluated on the Zero Resource 2017 challenge dataset, which consists of around 70 hours of unlabelled data. Our systems outperformed the baseline systems on all the languages without languagespecific parameter tuning. We performed comprehensive experiments of the system parameters on the system performance.

Virtual Phone Discovery for Speech Synthesis Without Text The objective of this work is to resynthesize speech directly from the speech signals without using any text in a different speakerxe2x80x99s voice. The speech signals are transformed into a sequence of acoustic subword units or virtual phones which are discovered automatically from the given speech signals in an unsupervised manner. The speech signal is initially segmented into acoustically homogeneous segments through kernelGram segmentation using MFCC and autoencoder bottleneck features. These segments are then clustered using different clustering techniques. The cluster labels thus obtained are considered as virtual phone units which are used to transcribe the speech signals. The virtual phones for the utterances to be resynthesized are encoded as onehot vector sequences. Deep neural network based duration model and acoustic model are trained for synthesis using these sequences. A vocoder is used to synthesize speech in target speakerxe2x80x99s voice from the features estimated by the acoustic model. The performance evaluation is done on ZeroSpeech 2019 challenge on English and Indonesian language. The bitrate and speaker similarity were found to be better than the challenge baseline with slightly lower intelligibility due to the compact encoding.