Bengali Speech to Text for Enterprise Contact Centers

Today, a contact centre providing Bengali-language support has two options: it can pay people to transcribe calls manually, or it can use a general-purpose speech recognition engine, which has been mostly trained on clean, studio-quality audio. In both cases, the same problem occurs. The manual transcription doesn’t scale beyond a few hundred calls per day, and the generic ASR fails when dealing with 8kHz telephonic audio, background noise, and the code-switching between Bengali and English that is characteristic of the way Indian and South Asian customers actually speak during a call.

The speech-to-text (STT) engine called Akshara, used by Convozen, has been designed specifically to address this second issue: telephonic, real-world, code-switched Bengali speech, rather than the clean voice samples on which most ASR benchmarks are trained and tested.

Why Bengali Transcription Breaks in Contact Centres

Bengali is spoken by more than 100 million people in India and in the wider area, and it is frequently encountered in BFSI collections, insurance claims, e-commerce support, and discussions relating to real estate sales. At enterprise scale, this dialect is difficult to transcribe for the following three reasons:

• Channel noise: The calls are transmitted over compressed 8kHz telephony lines rather than through microphones, which are the kind of acoustic signals that most ASR models have been trained on.
• Code-switching: In one sentence, Bengali and English are often combined (for example, with loan words, product names, and confirmation numbers), which confuses machine learning models trained on monolingual data
• Compliance exposure: Quality assurance and regulatory audit coverage remain manual and incomplete in the absence of accurate transcripts, usually applying to only a small part of the total call volume.

The outcome is identical in all industries: the QA teams only examine a small number of calls, compliance risks are not picked up until an audit takes place, and the insight at the conversation level that is, what customers are really asking for, objecting to, or complaining about remains trapped in the audio, which no one has the time to listen to.

Built for Telephonic Speech, Not Studio Audio

The word error rate (WER), which is the percentage of words that an ASR system gets wrong when compared to a human transcript, is the standard method for measuring transcription accuracy. The majority of published ASR benchmarks assess WER using clean, read-aloud audio, whereas real contact centre calls are entirely different.

The system Akshara has been pre-trained using over 50,000 hours of audio recorded over the phone and has then been fine-tuned on more than 4,000 hours of contact centre data that has been manually annotated, to be able to deal with background noise, cross-talk, and the clipped, rapid speech which is typical of live customer calls. Bengali is one of the nine Indian languages which Akshara supports natively, the others being Hindi, English, Tamil, Telugu, Kannada, Malayalam, Gujarati and Marathi.

Bengali Speech Recognition Accuracy: Akshara vs Sarvam and ElevenLabs

The Convozen Akshara ASR Benchmark Report from February 2026 assessed Akshara alongside Sarvam Saaras v3 and ElevenLabs Scribe v2 using the same Bengali audio segments, the evaluation set being a combination of the public collection (Indic Voices and Vaani) and a proprietary telephonic benchmark drawn from actual contact centre recordings.

When the data from the combined evaluation is examined, Akshara’s Bengali word error rate is 18.7% lower than that of Sarvam Saaras v3 and 40.9% lower than that of ElevenLabs Scribe v2; the difference becomes even greater on the telephonic-only benchmark, this benchmark reflecting the actual audio conditions found in contact centres rather than clean read speech, in which the competitors’ error rates rise by about double or triple.

Turning Bengali Transcripts into Actionable Conversation Intelligence

The accurate Bengali transcripts are the input; what enterprises need is what happens to them afterwards:

• The conversations can be searched. Each Bengali call is converted into a text record so that it can be searched, filtered, and reviewed rather than having to be re-listened to.
• Coverage of both quality and compliance is almost complete. Rather than manually auditing a small number of calls, conversations can now be automatically assessed against quality and compliance criteria on a large scale
• Conversation analytics transcripts are used for sentiment tracking, objection analysis, and producing voice-of-customer reports, all of which involve a volume that cannot be achieved through manual review.
• The workflow integrates by having the transcripts and the results of the interactions be fed into CRMs, data warehouses, and dashboards for use in subsequent reporting.

Since the STT stage is included within Convozen’s wider real-time pipeline and itself takes about 100 milliseconds to complete, transcription does not become the bottleneck in live-call scenarios such as real-time agent assist.

Enterprise-Grade Architecture

The Bengali transcription feature on Convozen is based on the same MSOC (Multi-Session Omni-Channel) architecture that is used for the rest of the platform, so if a conversation in Bengali starts on a call and then continues on WhatsApp, it keeps its context rather than having it reset. The underlying infrastructure features VAPT-audited security and follows ISO and GDPR/HIPAA guidelines for data handling, with SOC 2 compliance currently being carried out. Convozen handles 40 million or more voice AI calls and examines 50 million or more conversations each month throughout its customer base.

See Bengali Speech to Text in Action

Contact our team to discuss converting the conversations of Bengali customers into accurate and searchable transcripts for use in quality assurance, compliance, and analytics. Book a Demo