In machine learning, to improve the accuracy of a (pre-trained) model in a target domain, it must be retrained with additional domain-specific data. This practice is known as domain adaptation. In this article, we will dive into a specific type of domain adaptation: fine-tuning a Speech Recognition (ASR) model. Fine-tuning implies readjusting the weights of a pre-trained speech-to-text model, using new data, to steer it in a direction of interest. As a result, we get ‘better’ models that are adapted to new languages, accents, environments, and specialized domains such as healthcare, education, politics or sports. Depending on its architecture and type of training mechanism (supervised or unsupervised), the model can consume either or both audio and transcripts for adaptation. Fine-tuning also comes with additional computational costs, as it requires steps such as data cleaning, preprocessing, batching, feature extraction, model training, and evaluation. However, these computational costs are significantly lower than training a new model from scratch. Consequently, such model enhancement introduces its own challenges: availing the required/new data, cleaning and filtering data for noise and clutter, insufficient data to reach the desired accuracy, overfitting and catastrophic forgetting.
Fine-Tuning and the Universe: An Analogy
To put the idea of fine-tuning into context, consider the fascinating story of the Universe. According to the Standard model of Cosmology, our Universe originated from a state of initial singularity, a phase of infinite density before ‘quantum fluctuations’ triggered the Big Bang. It has been expanding for about 14 billion years, reaching its current “observable” form, covering nearly 93 billion light years. The Universe, its dynamic expansion, and evolution of its matter-energy content are governed by a set of parameters that define the standard model itself. There’s a well-known hypothesis suggesting that if some of these parameters had not been fine-tuned precisely in the early Universe, we simply could not have existed today. This makes us fortunate to be a part of this fine-tuned Universe. (If this interests you, here’s further reading to keep the thoughts following: Paper, Youtube Explainer)
Now, with this interesting fine-tuning story in mind, let’s dive into the topic of fine-tuning ASR models. Please note that fine-tuning any deep learning model is a broad topic beyond the scope of this article. Therefore, we will focus specifically on speech-to-text models.
The Importance of Fine-Tuning in ASR
In today’s fast-paced world, driven by Large Language Models (LLMs) and Generative Artificial Intelligence (Gen-AI), fine-tuning model parameters or weights remains a crucial task. We are familiar with Automatic Speech Recognition (ASR) systems, which help machines ‘recognize’ human speech. Here, ‘recognize’ corresponds to a broader context, involving detection of spoken utterances, their phonetic recognition, and conversion into text in natural language. Since the very first basic ASR models, such as “Audrey” and “Shoebox” (1952, 1962 Bell Labs & IBM), which only recognized a few digits and letters in English, ASR has revolutionized human-machine interactions, bridging the gap between human language and machine understanding (Want to learn more? A detailed insight on ASR and its evolution can be found here).
While the development of state-of-the-art ASR systems has reached impressive milestones (e.g., Whisper, Meta-Speech-to-Text, Speechbrain, Nemo Canary), accuracy improvements remain highest for well-resourced languages such as English, Mandarin, Spanish, French, German etc. Therefore, fine-tuning these models for specific tasks or low-resource languages remains a critical step in unlocking their full potential. Through this article, we will explore the basics, methodology, and challenges of fine-tuning ASR models, especially for low-resource languages.
Model Adaptation: Why Fine-Tuning?
Interestingly, fine-tuning in the Universe could be seen as a natural process without any ‘human supervision’. When we think about fine-tuning in the context of AI, a helpful analogy is to imagine the pre-trained model as a pot made of clay. The pot has already been roughly shaped – it’s functional but lacks the final design details. During fine-tuning, you work on adding those finishing touches, such as decorative patterns or texture, to personalize the pot for a specific purpose. You shape the existing clay, but you do so in a targeted way, improving the design without changing the core structure. Once the fine-tuning process is complete, the changes remain fixed – the pot is now fine-tuned to meet specific needs.
This process is illustrated in Figure 2 above, where the pre-trained model is represented as a basic, unadorned pot. The fine-tuning process adds new data and reshapes the model’s specific features (the “modified weights”) to make it fit the target domain of interest, just like adding decorative elements to a pot. The original design (the “initial weights”) remains intact, but it now includes new, refined elements.
Fine-tuning is an approach that doesn’t require “reinventing the wheel.” You can start with a pre-trained ASR model checkpoint and add a randomly initialized output layer on top of the existing architecture. By adjusting the weights of this layer with new, domain-specific data, the model adapts to new tasks. For example, a general speech recognition model can be fine-tuned on medical-related audio and text to better understand medical terminology. Similarly, using data from a regional radio or TV station can help create a model that recognizes regional dialects and accurately captures the local vernacular.
This approach is especially beneficial for addressing real challenges such as heavy accents, speech impediments, regional dialects, keyword detection in criminal investigations, children’s speech, or conversations between multiple speakers in noisy environments. Before diving deeper into the methodology, let’s look at some of the advantages of fine-tuning.
Advantages of Fine-Tuning ASR Models
- Enhancing Knowledge Base: Fine-tuning enhances the model’s original knowledge base, enabling it to learn from both existing and new data sources, improving performance in the new domain.
- Cost-Efficiency: Smaller, fine-tuned models can be more cost-effective than using larger, contemporary deep learning models for specific tasks, offering substantial cost savings and resource efficiency.
- Low-Cost Development: Fine-tuning facilitates low-cost development on open-source models, giving small businesses the opportunity to grow within the domain.
In addition to fine-tuning, other model adaptation methods may be more suitable depending on the use case. For example, cross-lingual transfer (transfer learning) adapts models or resources from higher-resource languages to low-resource languages. Vocabulary Adaptation helps update domain-specific lexicons with new words and their phonetic representations, allowing the recognizer to incorporate new words without requiring acoustic data or retraining the acoustic model.
Other adaptation techniques, such as vector adaptation, residual adapters, low-rank adapter (LoRA), and prompt-tuning, are used for efficient domain adaptation. Among these, matrix- reduced adapters have outperformed others in terms of speed and efficiency. However, when robustness is critical, fine-tuning remains the most effective method.
How Fine-Tuning Works
Fine-tuning an ASR model is much like baking a perfect cake – it requires careful attention to detail and precision. While temperature control (or, in this case, gradients) is crucial, the ingredients and preprocessing are equally important for achieving the best output. Similarly, adapting a speech-to-text model involves more than just adjusting the parameters. Traditional ASR systems consist of separate components, such as the Acoustic Model, Lexicon Model, and Language Model, that work together to transcribe human speech to text. The decoder used patterns of speech from Acoustic Model (e.g., GMM-HMM), word predictions from the Language Model (e.g., n-gram LM), and Finite State Transducer (FST) with a Pronunciation Dictionary to create the Phonetic Lexicon (For a deeper dive into how ASR systems have evolved check out this blog article). To improve robustness and accuracy of the ASR model, fine-tuning these individual components independently would be a complex task. In particular, one cannot afford to lose the mapping between HCLG framework (the backbone of the text prediction system) while attempting to enhance the model’s performance in a specific domain. As shown in the illustration below, the fine-tuning process has evolved considerably over the last two decades to simplify and optimize these adjustments.
How Modern ASR Fine-tuning Works?
Modern-day ASR fine-tuning has become much more accessible. AI-based copilots, like those found on platforms such as GitHub, allow users to write fine-tuning scripts in minutes – provided the prompts are correctly formulated. This accessibility simplifies the process, but fine-tuning still requires a solid understanding of model architecture and the task at hand, in some cases even the linguistic origin and relationships. For example, for fine-tuning on low-resource languages, one can strategically leverage the fine-tuning from high-resource language (for both data and model), to improve the model’s accuracy despite the scarcity of data.
However, the real challenge in state-of-the-art ASR fine-tuning, which often relies on sequence-to-sequence training, is improving the mapping between input audio sequences and generated word sequences. The acoustic model, typically the encoder, decomposes the audio signal, identifies various speech patterns, and maps these to probable phonemes. The language model, such as the one in Whisper, acts as the decoder and generates the text sequence. Whisper uses attention mechanism to understand the acoustic model and predict the next word in the sequence. The rules of the language model are based on the Natural Language Processing (NLP), which helps form complete and accurate sentences. Each encoder-decoder module in the model uses deep-learning architectures with multiple layers.
The encoder weights are initially tuned on a larger generic dataset, and the model is then further trained using pre-trained weights as a starting point with a new, domain-specific dataset. This allows the decoder layers to be adjusted to achieve better transcription accuracy.
Fine-Tuning as a Vital Process
In this part, we explored the concept of fine-tuning ASR models, highlighting its advantages, methodology, and real-world application. Fine-tuning makes ASR models more adaptable, improving accuracy while reducing computational cost. In the second part of this blog post, we will delve deeper into the challenges and opportunities fine-tuning presents, especially for low-resource languages.
In the upcoming blog post, we will continue exploring fine-tuning, but focus on the specific challenges that emerge when working with low-resource languages. Many languages around the world lack the necessary data and resources to create effective ASR models, and this can limit the applicability of speech recognition systems. We’ll examine data scarcity, the diversity of dialects, and resource constraints that make fine-tuning difficult in these contexts.
Additionally, we’ll share strategies to overcome these challenges, including techniques like data augmentation, cross-lingual transfer, and community engagement. These methods can help adapt ASR models for languages that are traditionally underrepresented in the tech world. Finally, we’ll take a look at how the future of ASR fine-tuning holds the potential to bridge the gap for low-resource languages, expanding the reach and inclusivity of speech recognition technology globally.
Stay tuned for part two as we address how fine-tuning can make ASR models more effective and accessible, even for languages that have previously been overlooked.
