Ifeoluwa Oduwaiye
Jul 18, 2025
When it comes to scaling effective voice agents, the quality of your speech transcription model can make or break your product. No matter how intelligent your agent is, it will continue to produce subpar output if it receives subpar input, and in most cases, the quality of the input it receives is determined by the transcription model.
This blog is the third in a series on Agentic AI. Here, we would explore speech transcription services, with a focus on the role they play in building effective AI Agents. We would also discuss how to measure and improve transcription accuracy, and some of the top-performing transcription services as of 2025.
Why we need High‑Quality Transcription Models
Mistranscriptions in STT models can be very costly for businesses and annoying to the users of such software. A slight error can greatly affect the results of the model. For instance, “Book me a vacation in Iraq” and “Book me a vacation in Iran” could cause major complications in hotel booking systems.
This is because the STT layer precedes all other systems in voice AI systems. As such, any errors in speech transcription would be propagated through the NLU (Natural Language Understanding), RAG (Retrieval Augmented Generation), response generation, and speech generation systems. These errors could significantly degrade customer experience and negatively impact business KPIs.
In high-stress use cases such as customer support, health, and even personal assistance, transcription fidelity directly affects the outcomes of these agentic systems.
Measuring Transcription Accuracy
When measuring the accuracy of transcription systems, two key metrics are commonly used:
Word Error Rate (WER): WER measures the percentage of words that were incorrectly predicted in a transcript. It is computed using the Levenshtein distance, which calculates the minimum number of edits, insertions, deletions, and substitutions needed to change the predicted word sequence into the reference sequence.
Character Error Rate (CER): CER works the same way as WER but operates at the character level instead of the word level. This is especially useful for languages with no spaces between words (like Chinese) or for evaluating fine-grained recognition accuracy, such as spelling and punctuation.
WER/CER = (S +D+I) / N
where;
S = number of substitutions
D = number of deletions
I = number of insertions
N = total number of words in the reference transcript
Although both WER and CER have the same formula, there are instances where WER is a favoured metric over CER and vice versa. WER is popularly used for measuring general-purpose transcription systems, while CER is used when dealing with short-form text or when it is important to capture fine-grained transcription quality.
Strategies to Improve Accuracy
Here are some strategies and techniques you can use to improve the accuracy of your transcription model.
Data Preprocessing: The quality of an STT model is strongly influenced by the quality of data passed into it or used to train the model. Keen attention needs to be paid to the training data. Augmentation techniques such as controlled noise injection, speed perturbation, ensuring that the transcripts used to train are of high quality, and diverse speaker sampling can go a long way in increasing the accuracy of STT models.
Model Fine-Tuning: This can be implemented by adapting base models to domain-specific vocabulary via transfer learning and custom lexicons. Fine-tuned models generally perform better than untuned ones because they are able to adapt to the dataset’s peculiarities.
Architectural Advances: A lot of advancements have been made in the research field, particularly in the realm of model architectures. This advancement is seen in architectures like Conformers, Hybrid Transformer-RNN models, and HMM-DNN tradeoffs for balancing latency and accuracy.
Post-processing & Error Correction: Language‑model rescoring, punctuation restoration, and context‑aware correction pipelines could also help in increasing the accuracy of transcription models.
Top Performing Commercial Transcription Services
One way to get started with integrating a transcription model into your agentic pipeline is to build one from scratch. This is often resource-intensive and has a long development time, as a lot of resources and time could be spent with a chance of not getting the desired result.
Another method is to integrate a 3rd party transcription service into your pipeline. While this method is not without its drawbacks, it offers a faster integration curve with enviable performance. If you favour the latter option, here are some of the top-performing transcription services in the market.
Google Speech‑to‑Text
Google’s Speech‑to‑Text API delivers highly accurate transcription powered by the same neural‑network models behind Google Assistant and YouTube captions. It supports both batch and real‑time streaming modes, automatic punctuation, word‑level confidence scores, and speaker diacritization for multi‑speaker audio.
With support for over 125 languages and variants, including many low‑resource dialects, alongside features like profanity filtering and automatic punctuation, it’s a versatile choice for global-scale transcription projects.
AWS Transcribe
AWS Transcribe offers both batch and real‑time streaming transcription through HTTP/2 or WebSocket APIs, making it easy to integrate live captions and voice‑enabled features into applications. It includes specialized variants like Amazon Transcribe Medical for clinical dictation and a NarrowBand model optimized for telephone audio, alongside powerful speaker‑labeling and diarization that can distinguish and tag individual speakers in multi‑participant recordings.
Users can further tailor results with custom vocabularies, profanity filtering, PII redaction, and multi‑channel support, and benefit from coverage of over 30 global languages and dialects, from Japanese and Turkish to Gulf Arabic and Swiss German.
Azure Speech
Microsoft’s Azure Speech service brings together speech‑to‑text, text‑to‑speech, translation, and speaker‑recognition under one umbrella, with particularly strong noise‑robustness and microphone‑array support for challenging acoustic environments. Its Custom Speech capability lets organizations train models on proprietary audio to boost accuracy for industry‑specific terminology.
Azure Speech can seamlessly be integrated with other Azure AI offerings for applications in real‑time speech translation and intent recognition. This is a great choice if you are building on Azure.
OpenAI Whisper API
OpenAI’s Whisper API leverages a massive, diverse training corpus to deliver strong out‑of‑the‑box performance on accented speech, technical jargon, and background noise. The API supports 99 languages and offers open‑source C++ variants like whisper.cpp for on‑premises deployment.
Although Whisper tends to “hallucinate” in silent or highly specialized passages, it is best suited for low‑risk transcription tasks or as a base for further human‑in‑the‑loop correction.
Spitch
Spitch stands out for its Africa-centric model coverage and end-to-end voice AI platform, encompassing speech-to-text, text-to-speech, machine translation, and tone-marking. It supports English alongside low‑resource African languages such as Yoruba, Hausa, Igbo, and Amharic, each with multiple high‑quality TTS voices.
The platform offers an affordable pricing model with unique voices that can be easily integrated into agentic pipelines. Spitch is a compelling choice for organizations seeking specialized support in emerging markets.
Concluding Remarks
High‑quality transcription is the backbone of any voice‑driven solution. Accurate, context‑aware transcripts not only boost the reliability of AI agents but also unlock richer analytics, stronger compliance, and more natural user interactions. When choosing a provider, it is important to consider factors like word‑error rate, language coverage, customization options, latency, and built-in enrichments.
Integrating Spitch’s TTS and STT into your LiveKit voice agents is now effortless. Explore our guide to get started with integrating Spitch into your voice agents for a seamless native experience with your customers.
