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.

Speaker Embedding Extraction with Virtual Phonetic Information In the recent past, deep neural networks have been successfully employed to extract fixeddimensional speaker embeddings from the speech signal. The commonly used xvectors are extracted by projecting the magnitude spectral features extracted from the speech signal onto a speakerdiscriminative space. As the xvectors do not explicitly capture the speakerspecific phonological pronunciation variability, phonetic vectors extracted from an automatic speech recognition (ASR) engine were supplied as auxiliary information to improve the performance of the xvector system. However, the development of ASR engine requires a huge amount of manually transcribed speech data. In this paper, we propose to transcribe the speech signal in an unsupervised manner with the cluster labels obtained from a mixture of autoencoders (MoA) trained on a large amount of speech data. The unsupervised labels, referred to as virtual phonetic transcriptions, are used to extract the phonetic vectors. The virtual phonetic vectors extracted using MoA are supplied as auxiliary information to the xvector system. The performance of the proposed system is compared with the stateoftheart xvector system on NIST SRE2010 data. The proposed unsupervised auxiliary information provides a relative improvement of 12.08%, 3.61% and 16.66% over the xvector system on corecore, core10sec and 10sec10sec conditions, respectively.