Deep Dive into Speech-to-Text Models: How They Work and How to Improve Accuracy

Introduction

Speech-to-Text (STT) models have been around since the 1950s and have now seen applications in a wide range of industries. While some see it as a thing of the past, it remains a powerful system that, when integrated properly, can improve accessibility, act as an intermediary between humans and machines, and much more.

One of the first-ever developed STT models was IBM's Shoebox, introduced in 1962. It could recognize 16 spoken words and digits 0-9, marking an early step in voice recognition technology. However, it was limited by vocabulary size and computational power. Listen, Attend and Spell (LAS) came out after Shoebox in 2016, offering users end-to-end speech recognition through its bimodal architecture.

More recently, we have DeepSpeech (by Mozilla) and Whisper (by OpenAI), which have significantly improved upon the limitations of earlier models. DeepSpeech, based on Baum-Welch algorithms and deep learning, introduced end-to-end speech recognition, allowing for better accuracy. Meanwhile, Whisper trained on 680,000 hours of multilingual audio data, is capable of handling multiple languages, accents, and noisy environments, making it one of the most advanced open-source STT models today.

Another recent innovation in the STT field is the Conformers (Convolution-augmented Transformer for Speech Recognition). Introduced in 2020, they combine Convolutional Neural Networks (CNNs) and Transformers in capturing both detailed short-term speech patterns and big-picture long-range dependencies. Due to its bi-directional approach, conformers can accurately recognize speech in various conditions, making them one of the most powerful architectures for modern Automatic Speech Recognition (ASR) systems.

Conformer Architecture (Source)

With the advanced state of STT models, one might think that all the work is done and that there is no need to improve. Well, I am of the standpoint that it all depends on your strategy and tactic. If you’re looking to build a Speech-to-Text model in 2025, then this article is for you. Here, we will cover STT models, how they work, how to improve their accuracy, and why Spitch is the best bet for your STT needs. Stay tuned!

How Speech-to-Text models work

The fundamental thing behind STT models is pipelines. Think of an STT pipeline as a step-by-step process that handles how audio is converted into text. It defines how data moves through different stages, gets processed, and how the final transcription is generated—both when training the model and when using it in real time. 

STT models might seem a bit technical at first but trust me, once you understand the fundamental structure, you're pretty much good to go. This structure often varies depending on the use cases and the purpose of the STT model. The STT model pipeline typically includes:

1. Audio input & preprocessing: As the name implies, Speech-to-Text models convert speech to text. Thus, a huge chunk of the efficiency of these models lies in the quality of the audio input. A major preprocessing step performed here is Noise reduction. Noise reduction is very important in removing unwanted signals that might tamper with the model's inference. Audio segmentation is also another important preprocessing step performed on audio input.

2. Feature extraction: Before the processed speech can be converted into text, the audio signal must be transformed into a format that a Machine Learning (ML) model can process. Hence, the need for feature extraction. Feature extraction techniques vary across different models. For example, Whisper processes audio as Mel Spectrograms while Wav2Vec processes audio directly in their raw audio waveforms. 

Two widely used techniques for audio feature extraction are MFCC and Spectrograms.

1. Mel-Frequency Cepstral Coefficients (MFCCs): This technique involves breaking down an audio signal into short time frames and extracting the spectral features that mimic how people perceive sound. It helps the model differentiate between phenomes.

2. Spectrograms: These reproduce a visual representation of the frequency of sound over time. It captures the pitch, tone, and rhythm of a sound.

Audio Waveforms (Source)

3. Acoustic modelling: Before the STT model can be developed, an acoustic model needs to come first. Acoustic models help to map extracted audio features to phenomes (subword units that make up words). Without a successful phenome mapping, the language model accuracy would suffer. 

There are two main methods used in this step of the pipeline:

1. Traditional Methods: These methods consist of Hidden Markov Models (HMMs) and Gaussian Mixture Models (GMMs) in predicting phenomes from extracted audio features.

2. Deep Learning-Based Approaches: Some of the models used in this approach are Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), Convolutional Neural Networks (CNNs), and Transformer-based models like Wav2Vec, Whisper, XLS-R, or Conformers.

4. Language modelling: In the STT pipeline, the language model works to ensure that the system’s transcriptions make sense to the reader. Concepts such as linguistics, order of words and grammar, come into play here. With probability-based text predictions, the model can output words based on the probability of it occurring next in a sentence. Two popularly used approaches here are N-gram models, Transformer-based language models, and RNNs.

5. Post-processing & error correction: Even with the advancement in language modelling, STT models are not exempt from errors in inference, and post-processing is needed to enhance model accuracy. Some of the common issues that need to be checked in STT models are:

   1. Spell-Checking

   2. Error Correction

   3. Bias Mitigation

   4. Accent Adaptation

Types of Speech-to-Text models

Three of the most common STT modelling techniques are RNN-T (Recurrent Neural Network Transducers), CTC (Connectionist Temporal Classification), and Seq2Seq (Sequence-to-Sequence) models.

1. RNNT: These models are optimized for real-time transcription by using a combination of an encoder, a prediction network, and a joint network to align audio dynamically with text. Compared with traditional CTC models, RNNT models excel at handling variable-length speech input efficiently as opposed to CTC’s limitation to fixed alignment between input frames and output tokens. 

   The prediction network acts like a language model, incorporating past context to refine transcriptions, making RNNT well-suited for streaming speech recognition where low latency is critical.

2. CTC: These models predict characters or words independently for each time step and then use a collapse function to align them into a final transcription, making them computationally efficient. Due to these independent character predictions, CTC models turn out to be less context-aware. 

   Since CTC relies on a many-to-one mapping of input frames to output tokens, it struggles with long-range dependencies and complex linguistic structures. However, its ability to handle unaligned data makes it useful for scenarios where precise frame-level annotations are unavailable, and it is often paired with external language models to improve contextual understanding.

3. Seq2Seq with Cross-Entropy Loss: Seq2Seq models use an encoder-decoder architecture with attention mechanisms to generate entire sentences, allowing them to capture long-term dependencies and contextual relationships more effectively than CTC or RNNT. Although Seq2Seq models turn out to be more computationally intensive, they differ from frame-based models by directly mapping input sequences to output sequences without requiring strict temporal alignment.

   Seq2Seq relies on cross-entropy loss to optimize token predictions at each decoding step, often benefiting from transformer-based architectures to enhance their ability to model complex speech patterns and linguistic structures.

Factors that affect Speech-to-Text accuracy

Now that we understand how Speech-to-Text models work, it’s important to explore the key factors that influence their accuracy. While modern STT models leverage deep learning and language modelling techniques, their performance is often affected by various external and linguistic challenges.

1. Background noise & recording quality: One of the biggest factors that impact speech technology and ultimately STT models is the quality of the audio input. Noisy environments, poor microphone quality, and overlapping speech can interfere with the predictions of the most powerful models. High-quality recordings with minimal background noise can significantly improve automatic speech recognition accuracy. 

2. Speaker variability: Accents and regional dialects present another major hurdle to STT accuracy. Most STT models are trained on high-resource languages like English, often favouring Western accents. While this may reach a higher percentage of people, it doesn’t promote inclusivity and could increase bias against minority speakers. In multilingual regions like Africa, where accents and dialects vary significantly, STT models must be trained with diverse speech data to transcribe spoken words accurately. Variations in pronunciation and speech speed are also other factors that could also affect automatic speech recognition accuracy.

3. Vocabulary & language complexity: Varied vocabularies and language complexity also play a role in transcription performance. Some languages have extensive homophones, while others are tonal and require models to interpret meaning based on pitch. STT models also struggle and could produce incorrect transcriptions when transcribing low-resource languages or words outside their trained vocabulary.

4. Domain-specific challenges: STT models often fail in highly specialized domains, such as medical transcription or legal proceedings, where industry-specific terminology is frequently used. Generic speech models may not recognize specialized vocabulary, leading to inaccurate transcriptions. For these unique use cases, STT models would have to be fine-tuned for specific industries to achieve better results.

How to Improve Speech-to-Text Accuracy

If you’re looking to build an STT model, here’s how you can work smart and build it without meeting any of the challenges listed above. To enhance the accuracy of STT models, researchers and developers employ various strategies to address challenges.

1. Using AI-enhanced noise reduction & speaker adaptation: Modern STT systems now leverage AI-driven noise suppression techniques to filter out background noise and improve the quality of audio recordings. Additionally, speaker adaptation techniques can improve recognition by adjusting transcription models to different voice profiles, speech speeds, and accents over time.

2. Training with diverse datasets: One of the most effective ways to improve STT accuracy is by expanding training datasets to include a wide variety of speakers, accents, and languages. Doing this promotes inclusion in STT models, and increases the scope of the model’s vocabulary. 

3. Fine-tuning language models for specific industries: Generic STT models perform poorly in domains with specialized vocabulary. Fine-tuning these models for fields like healthcare, finance, legal, and customer support can ensure that the transcription engine understands industry-specific jargon, and improve the quality of transcription in the long run.

4. Implementing real-time corrections & human-in-the-loop verification: Despite improvements in AI models, human oversight is still essential. Implementing real-time correction systems and human-in-the-loop (HITL) verification ensures that STT outputs maintain high accuracy, especially for critical applications like legal transcription or medical documentation.

The Challenges of Speech-to-Text Technology for Low-Resource Languages

While STT technology has made significant progress for high-resource languages like English, Spanish, and Mandarin, low-resource languages, are often left out of this loop and their speakers face unique challenges with using this software. In a country like Africa that has over 3,000 languages, only a tiny fragment of these languages are represented in STT models.

These speakers are then faced with learning a new language or being left entirely out of the AI chat. 

Why most STT models fail for African languages

Most commercial STT systems are optimized for major languages and are not inclusive of African languages. When trying to transcribe African-accented English most models fail for reasons that are not so far-fetched. These reasons are listed below. 

1. Lack of large labelled datasets: Unlike high-resource languages like English and Mandarin which have millions of hours of speech data available for training, African languages have significantly fewer datasets. This level of data availability opposes the design or development of STT models for African languages. 

2. Unique phonetic structures & tones: A huge percentage of African languages are tonal. This means that the meaning of a word depends strongly on how it is pronounced (its tone). Widescale STT models primarily focus on Western languages and fail to capture these tonal variations, leading to transcription errors.

3. Limited investment in AI for non-Western languages: Most AI research and STT model development focus on languages spoken in economically powerful regions, leaving low-resource languages underfunded and overlooked. This lack of funding also discourages developers and entrepreneurs from building AI and STT technology for African languages. This results in a lack of support for African languages in mainstream STT applications.

4. The digital divide & its impact on AI voice technology adoption: In many African regions, access to computers and the internet is limited. This poses issues in deploying real-time transcription solutions as cloud-based STT solutions could struggle with latency issues in low-bandwidth environments.

How Spitch is Unlocking Speech-to-Text for African Languages

Speech-to-Text technology has become an essential tool in various industries, from customer service automation to media transcription and accessibility solutions. However, for low-resource languages, particularly in Africa, most STT models fail to deliver accurate results due to a lack of high-quality training data, phonetic complexity, and dialectal diversity. 

Spitch is revolutionizing this space by providing cutting-edge STT models tailored specifically for African languages. One of the biggest challenges in STT for African languages is the scarcity of large, labelled datasets. Many automatic speech recognition systems rely on millions of hours of transcribed audio for training, but African languages lack open-source datasets of this scale. 

Spitch addresses this issue by curating, annotating, and training its AI models on high-quality speech data from native speakers. By focusing on real-world linguistic diversity, Spitch ensures that its models can handle regional accents, dialects, and tonal variations effectively.

The Impact of Spitch on Accessibility and Digital Inclusion

In Africa, where literacy levels vary and many people prefer voice communication over text, STT technologyis a key factor in digital inclusion. Spitch’s transcription models enable better accessibility for individuals with disabilities, support multilingual customer service, and enhance content creation for African audiences. 

By providing accurate STT solutions for African languages, Spitch is helping to close the digital divide and foster greater participation in the global digital economy. Spitch is committed to making AI-driven STT technology more accessible to African businesses and developers through its easy-to-integrate API solutions, real-time, low-latency transcription, and tone-sensitive language models.

Spitch Playground

Concluding Remarks

The future of STT is evolving with breakthroughs in self-learning models, zero-shot learning models, and multilingual AI frameworks that support seamless code-switching between languages. Spitch is at the forefront of language technology innovation in Africa and is expanding its STT capabilities across more African languages, ensuring that linguistic diversity is fully represented in AI-driven speech recognition.

By integrating Spitch’s AI-powered speech transcription, businesses and developers can enhance accessibility, streamline workflows, and create new opportunities for voice-driven innovation across Africa. Want to power your applications with the best STT models for African languages? Start using Spitch’s Speech-to-Text API today and take your voice-powered applications to the next level!