# Regularized Urdu Speech Recognition with Semi-Supervised Deep Learning
## 1 Introduction  
Automatic Speech Recognition (ASR) can be a vital component in artificially-intelligent interactive systems. After more than 50 years of research, ASR is still not a completely solved problem. ASR consists of three main components. These are, (a) extraction of useful features from speech for recognition of language phonemes or words, (b) classification of extracted features into words and (c) probabilistic modeling of predicted words based on language grammar and dictionary. The most famous features used for ASR are the Mel Frequency Cepstral Coefficients (MFCC). MFCC are found by taking the Discrete Cosine Transform (DCT) of logarithm values of energies in filter banks applied on a Mel scale to Power Spectral Density (PSD) of speech. Lower MFCC coefficients correspond to slow changingfrequencies in sound and are used for speaker independent speech recognition as they represent vocal tract response creating phonemes. This work is focused on pattern recognition of extracted speech features to classify isolated Urdu words. Pattern recognition finds patterns in data to perform certain tasks, while processes to learn patterns present in data can be referred to as machine learning. All machine learning algorithms can be represented as a general model with tunable hyper parameters which are learned during the training phase using available data. The model with learned parameters is then used to perform required tasks in the test phase. Machine learning algorithms are generally categorized as supervised or unsupervised. Supervised learning algorithms use labeled data as the training set. For labeled data, each point is manually marked with a target label. Supervised learning tasks usually perform either classification or regression. Unsupervised learning algorithms learn features or patterns from unlabeled data. The unsupervised learning generally performs clustering, density estimation and dimension reduction tasks. Utilizing both supervised and unsupervised techniques for data classification is called Semi-Supervised Learning (SSL). ASR got its early success using the statistical pattern classification technique Gaussian mixture distributions with Hidden Markov Models (HMMs). Despite the earlier success of HMMs, they have the limitations of being complex and having unrealistic assumptions about distribution shapes of observed speech features. Recently, using neural networks for speech recognition has achieved the best results [1]. For neural networks to generalize well they need huge amounts of labeled training data. Most of the English ASR research utilizes the Texas institute global corpus, which is used and updated by researchers around the globe. Such resources are missing for low-resource languages like Urdu. Recently the Urdu Corpus has been developed containing 250 isolated words spoken by 10 different speakers and is being used for Urdu ASR research [2]. [PERSON] et al. reported 25% mean Word Error Rate (WER) using HMM models for speech recognition on the corpus in speaker independent setup with 90% of speech used as training and 10% as test data [3]. This paper describes the performance of an end-to-end neural network-based speech recognition model tested on the same corpus. The model uses dropout, ensemble averaging, Maxout and SSL for better generalization with limited data. The proposed model achieves a mean WER, 4% lower than [PERSON] et al. [3]. The model is also tested using as low as 50% of the available corpus as training data for the first time and the performance does not deteriorate drastically with the limited training data portion because of SSL. This is quite significant for low-resource languages like Urdu. The rest of the paper is organized as follows. In Section 2, optimization and regularization techniques, recently proposed, for deep learning have been reviewed and SSL techniques like Label Propagation and Locally Linear Embedding (LLE) are elaborated. The proposed model is discussed in Section 3.