面向嘈杂电话环境的语码切换乌尔都语ASR：采用混合HMM与CNN-TDNN的数据中心方法
Code-Switched Urdu ASR for Noisy Telephonic Environment using Data Centric Approach with Hybrid HMM and CNN-TDNN
AI总结：
针对嘈杂呼叫中心场景下乌尔都语语码切换ASR资源受限的问题，本文提出混合HMM与CNN-TDNN的高效框架，结合数据标注优化，在干净/嘈杂环境的孤立词与连续语音中均实现5.2%的WER。
摘要：
呼叫中心拥有海量音频数据，可用于挖掘宝贵的商业洞见，而人工转录电话通话是一项繁琐的工作。高效的自动语音识别（ASR）系统能够准确转录这些通话，便于按特定语境和内容检索通话记录，从而实现自动通话监控，并通过关键词搜索和情感分析提升服务质量（QoS）。由于电话环境通常存在噪声，呼叫中心场景的ASR需要更强的鲁棒性。此外，许多低资源语言正濒临消亡，可借助自动语音识别技术实现保护。乌尔都语是全球使用人数排名第10的语言，全球使用者达231,295,440人，但在ASR领域仍属于资源受限语言。区域呼叫中心的对话以当地语言进行，同时混杂英语数字和技术术语，通常会引发“语码切换”问题。为此，本文提出了一种资源高效的自动语音识别/语音转文本系统实现框架，面向嘈杂呼叫中心环境，针对语码切换乌尔都语采用链式混合HMM（隐马尔可夫模型）与CNN-TDNN（卷积神经网络-时延神经网络）架构。混合HMM-DNN方法使我们能够在标注数据较少的情况下发挥神经网络的优势。在TDNN中加入CNN已被证明在噪声环境下表现更优，因为CNN具备额外的频率维度，可从含噪语音中捕获更多信息，从而提升识别准确率。我们从多个公开来源收集数据，在分析了乌尔都语以及其他语言（主要是英语）常用词的通用语境和内容后，对部分未标注数据进行了标注，最终在嘈杂与干净环境下，针对孤立词/数字以及连续自发语音均实现了5.2%的词错误率（WER）。
英文摘要：
Call Centers have huge amount of audio data which can be used for achieving valuable business insights and transcription of phone calls is manually tedious task. An effective Automated Speech Recognition system can accurately transcribe these calls for easy search through call history for specific context and content allowing automatic call monitoring, improving QoS through keyword search and sentiment analysis. ASR for Call Center requires more robustness as telephonic environment are generally noisy. Moreover, there are many low-resourced languages that are on verge of extinction which can be preserved with help of Automatic Speech Recognition Technology. Urdu is the $10^{th}$ most widely spoken language in the world, with 231,295,440 worldwide still remains a resource constrained language in ASR. Regional call-center conversations operate in local language, with a mix of English numbers and technical terms generally causing a "code-switching" problem. Hence, this paper describes an implementation framework of a resource efficient Automatic Speech Recognition/ Speech to Text System in a noisy call-center environment using Chain Hybrid HMM and CNN-TDNN for Code-Switched Urdu Language. Using Hybrid HMM-DNN approach allowed us to utilize the advantages of Neural Network with less labelled data. Adding CNN with TDNN has shown to work better in noisy environment due to CNN's additional frequency dimension which captures extra information from noisy speech, thus improving accuracy. We collected data from various open sources and labelled some of the unlabelled data after analysing its general context and content from Urdu language as well as from commonly used words from other languages, primarily English and were able to achieve WER of 5.2% with noisy as well as clean environment in isolated words or numbers as well as in continuous spontaneous speech.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
