This research describes an effort to build Urdu speech corpora for the banking sector in Pakistan. For further studies, the importance of the corpus and language model in the success of the Turkish ASR system is shown. The decrease in WER after including the new corpus was more evident with increased verified data size, compensating for the status of Turkish as a low resource language. In light of the test results of the other existing corpora, the current study showed the relative contribution of corpus variability in a symmetric speech recognition task. For training models, the newly collected corpus and other existing corpora published by the Linguistic Data Consortium were used. We also provide the initial speech recognition results of artificial neural network and Gaussian mixture-model-based acoustic models for Turkish. In the third step, a transfer learning approach to the Grand National Assembly of Turkey session records (videotext) was used. In the second step, data were collected via a mobile application. In the first step, subtitles, which are mainly supplied for people with hearing difficulties, were used as transcriptions for the speech utterances obtained from movies. In the presented approach, three different methods were used. In this study, we constructed a viable alternative to classical transcribed corpus preparation techniques for collecting Turkish speech data.

Therefore, development of a symmetric Turkish transcribed speech corpus according to the high resources languages corpora is crucial for improving and promoting Turkish speech recognition activities. Turkish is a language with limited resources for ASR.

Corpus preparation is the first step required for developing an ASR system for a language with few argument speech documents available. To build automatic speech recognition (ASR) systems with a low word error rate (WER), a large speech and text corpus is needed.

Urdu speeches in written form