Anlirika: An LSTM – CNN Flow Twister for Spoken Language Identification (2021)

Abstract: The paper presents Anlirika’s submission to SIGTYP 2021 Shared Task on Robust Spoken Language Identification. The task aims at building a robust system that generalizes well across different domains and speakers. The training data is limited to a single domain only with predominantly single speaker per language while the validation and test data samples are derived from diverse dataset and multiple speakers. We experiment with a neural system comprising a combination of dense, convolutional, and recurrent layers that are designed to perform better generalization and obtain speaker-invariant representations. We demonstrate that the task in its constrained form (without making use of external data or augmentation the train set with samples from the validation set) is still challenging. Our best system trained on the data augmented with validation samples achieves 29.9% accuracy on the test data.

## introduction among approximately 7,000 world languages, over 43% are oral only and do not exhibit any writing system. still, even in less exotic cases language processing systems may have to solely rely on vocal representations. spoken language identification (sli) is essential sub-task in many approaches to multilingual automated speech recognition and machine translation. in addition, it also has practical applications as a standalone task. automated assignment of a call center operator to a client is one of possible use case scenarios. the paper provides a description of "anlirika" system foot_0 that was submitted to sigtyp 2021 shared task on robust sli @xcite . in terms of the task, systems are trained to predict a language class (id) from an audio signal. importantly, the task aims at development of robust systems that can generalize well to new domains and speakers. many languages are under-resourced, and the situation when the language data exist only for a very limited number of speakers or domains is common. for instance, the largest multilingual sli dataset, namely cmu wilderness @xcite , has been derived from the bible in ≈ 700 languages and lacks speaker diversity. therefore, it is essential for a system to be speaker-invariant and robust. ## related work most work on sli focused on indo-european languages such as english, german, russian, french, hindi. it is also common to transform raw audio signal into the log-mel spectra or mfcc features. recent approaches such as @xcite , revay and teschke (2019), and @xcite make use of various convolution-based neural architectures. for instance, @xcite proposed a hybrid model that used convolutional layers to extract spatial features and recurrent units (bidirectional lstms) tp capture temporal characteristics. @xcite explored the resnet-50 @xcite architecture dynamically adapting learning rate. ## dataset the dataset comprises 16 typologically diverse languages from afro-asiatic, austronesian, basque, dravidian, indo-european, niger-congo, and tai-kadai families. the training data is derived from the cmu wilderness dataset @xcite which represents a single domain (speech utterances from the bible) and has predominantly a single speaker per language. the validation and test sets were collected from multiple corpora such as common voice @xcite and present a variety of recording conditions with multiple speakers per language. the length of each speech utterance ranged ## architecture as illustrated on figure 1 , we used a multi-layer neural network solution with two dense layers, one cnn and 1-7 lstm layers. the design of neural layer stack is motivated by the following general vision of how a sample should be processed: the layer stack we used is summarized in ## experiments we varied n l , the number of extra sequential lstm layers (which outputs were concatenated to the output of the cnn layer). we tried the following options: {0,2,4,6}. a number of units in each lstm layer was chosen from {200,300}. we used equal numbers of units across all the lstm layers present in the model.