The performance of data-driven natural language processing systems is
contingent upon the quality of corpora. However, principal corpus design
criteria are often not identified and examined adequately, particularly in the
speech processing discipline. Speech corpora development requires additional
attention with regard to clean/noisy, read/spontaneous, multi-talker speech,
accents/dialects, etc. Domain selection is also a crucial decision point in
speech corpus development. In this study, we demonstrate the significance of
domain selection by assessing a state-of-the-art Bangla automatic speech
recognition (ASR) model on a novel multi-domain Bangladeshi Bangla ASR
evaluation benchmark - BanSpeech, which contains 7.2 hours of speech and 9802
utterances from 19 distinct domains. The ASR model has been trained with deep
convolutional neural network (CNN), layer normalization technique, and
Connectionist Temporal Classification (CTC) loss criterion on SUBAK.KO, a
mostly read speech corpus for the low-resource and morphologically rich
language Bangla. Experimental evaluation reveals the ASR model on SUBAK.KO
faces difficulty recognizing speech from domains with mostly spontaneous speech
and has a high number of out-of-vocabulary (OOV) words. The same ASR model, on
the other hand, performs better in read speech domains and contains fewer OOV
words. In addition, we report the outcomes of our experiments with layer
normalization, input feature extraction, number of convolutional layers, etc.,
and set a baseline on SUBAK.KO. The BanSpeech will be publicly available to
meet the need for a challenging evaluation benchmark for Bangla ASR.

本研究中，我们通过评估一个最先进的孟加拉语自动语音识别模型，证明了领域选择的重要性，该模型在新的多领域孟加拉语 ASR 评估基准 - BanSpeech 上进行评估，其中包含来自 19 个不同领域的 9802 个话语。该 ASR 模型已经在 SUBAK.KO 上进行了训练，使用了深度卷积神经网络，层规范化技术和连接时序分类丢失准则，评估结果表明该 ASR 模型很难识别源于大部分自发性言论的领域的语音，并且包含的生词数量很多，在阅读语音领域表现更好并且包含更少的生词。