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.

本文介绍了在嘈杂的呼叫中心环境中，使用链式混合 HMM 和 CNN-TDNN 来构建资源高效的自动语音识别 / 语音转文本系统，以解决自动化语音识别在区域呼叫中心中的 “代码切换” 问题，以提高关键词搜索和情感分析的准确性，实现了 5.2% 的识别错误率。

利用基于数据的方法和混合 HMM 与 CNN-TDNN 实现噪声电话环境下的代码切换乌尔都语自动语音识别

Code-Switched Urdu ASR for Noisy Telephonic Environment using Data  Centric Approach with Hybrid HMM and CNN-TDNN

Customer experience plays a critical role for a profitable organisation or
company. A satisfied customer for a company corresponds to higher rates of
customer retention, and better representation in the market. One way to improve
customer experience is to optimize the functionality of its call center. In
this work, we have collaborated with the largest provider of telecommunications
and Internet access in the country, and we formulate the customer-agent pairing
problem as a machine learning problem. The proposed learning-based method
causes a significant improvement in performance of about $215\%$ compared to a
rule-based method.

研究旨在提高客户体验和呼叫中心功能的有效性，使用机器学习方法对客户 - 代理人分配问题进行建模并实现约 215％的表现提高。

智能客户 - 客服匹配在呼叫中心中提高客户体验

Improving Customer Experience in Call Centers with Intelligent  Customer-Agent Pairing

Banking call centers receive millions of calls annually, with much of the
information in these calls unavailable to analysts interested in tracking new
and emerging call center trends. In this study we present an approach to call
center theme detection that captures the occurrence of multiple themes in a
question, using a publicly available corpus of StackExchange personal finance
questions, labeled by users with topic tags, as a testbed. To capture the
occurrence of multiple themes in a single question, the approach encodes and
clusters at the sentence- rather than question-level. We also present a
comparison of state-of-the-art sentence encoding models, including the SBERT
family of sentence encoders. We frame our evaluation as a multiclass
classification task and show that a simple combination of the original sentence
text, Universal Sentence Encoder, and KMeans outperforms more sophisticated
techniques that involve semantic parsing, SBERT-family models, and HDBSCAN. Our
highest performing approach achieves a Micro-F1 of 0.46 for this task and we
show that the resulting clusters, even when slightly noisy, contain sentences
that are topically consistent with the label associated with the cluster.

本文提出了一种通话中心主题检测的方法，该方法捕捉到一个问题中多个主题的发生，并提出了一种以句子而非问题为级别的编码和聚类方法，以及一种多类分类任务的比较，表明了所提出的方法在其上表现出色。