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In many Indian languages, written characters are organized on sound phonetic principles, and the ordering of characters is the same across many of them. However, while training conventional end-to-end (E2E) Multilingual speech recognition systems, we treat characters or target subword units from different languages as separate entities. Since the visual rendering of these characters is different, in this paper, we explore the benefits of representing such similar target subword units (e.g., Byte Pair Encoded(BPE) units) through a Common Label Set (CLS). The CLS can be very easily created using automatic methods since the ordering of characters is the same in many Indian Languages. E2E models are trained using a transformer-based encoder-decoder architecture. During testing, given the Melfilterbank features as input, the system outputs a sequence of BPE units in CLS representation. Depending on the language, we then map the recognized CLS units back to the languagespecific grapheme representation. Results show that models trained using CLS improve over monolingual baseline and a multilingual framework with separate symbols for each language. Similar experiments on a subset of the Voxforge dataset also confirm the benefits of CLS. An extension of this idea is to decode an unseen language (Zero-resource) using CLS trained model.

In this paper, we discuss the benefits of using articulatory and stacked bottleneck features (SBF) for low resource speech recognition. Articulatory features (AF) which capture the underlying attributes of speech production are found to be robust to channel and speaker variations. However, building an efficient articulatory classifier to extract AF requires an enormous amount of data. In low resource acoustic modeling, we propose to train the bidirectional long short-term memory (BLSTM) articulatory classifier by pooling data from the available low resource Indian languages, namely, Gujarati, Tamil, and Telugu. This is done in the context of Microsoft Indian Language challenge. Similarly, we train a multilingual bottleneck feature extractor and an SBF extractor using the pooled data. To bias, the SBF network towards the target language, a second network in the stacked architecture was trained using the target language alone. The performance of ASR system trained with stand-alone AF is observed to be at par with the multilingual bottleneck features. When the AF and the biased SBF are appended, they are found to outperform the conventional filterbank features in the multilingual deep neural network (DNN) framework and the high-resolution Mel frequency cepstral coefficient (MFCC) features in the time-delayed neural network(TDNN) framework.

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