HMM Based Text-to-Speech Synthesis for Telugu

This thesis describes a novel approach to build a general purpose working Telugu text-to- speech synthesis system (TTS) based on hidden Markov model (HMM) which is reasonably intelligible, natural sounding and exible. There have been several attempts proposed to use HMM for constructing TTS systems. Most of such systems are based on waveform concatenation techniques. To fully convey information present in speech signals, text-to-speech synthesis systems are required to have an ability to generate natural sounding speech with arbitrary speakers individualities and emotions (e.g., anger, sadness, joy). To represent all these factors the Mel- cepstral coefficients are extracted as spectral parameters. Excitation parameters are extracted using fundamental frequency(F0)

Predicting Remaining Useful Life using Time Series Embeddings based on Recurrent Neural Networks

We consider the problem of estimating the remaining useful life (RUL) of a system or a machine from sensor data. Many approaches for RUL estimation based on sensor data make assumptions about how machines degrade. Additionally, sensor data from machines is noisy and often suffers from missing values in many practical settings. We propose Embed-RUL: a novel approach for RUL estimation from sensor data that does not rely on any degradation-trend assumptions, is robust to noise, and handles missing values. Embed-RUL utilizes a sequence-to-sequence model based on Recurrent Neural Networks (RNNs) to generate embeddings for multivariate time series subsequences. The embeddings for normal and degraded machines tend to be different, and are therefore found to be useful for RUL estimation. We show that the embeddings capture the overall pattern in the time series while filtering out the noise, so that the embeddings of two machines with similar operational behavior are close to each other, even when their sensor readings have significant and varying levels of noise content. We perform experiments on publicly available turbofan engine dataset and a proprietary real-world dataset, and demonstrate that Embed-RUL outperforms the previously reported state-of-the-art on several metrics.Comment: Presented at 2nd ML for PHM Workshop at SIGKDD 2017, Halifax, Canad