AI Transcription Done Right: 6 Techniques for Improved Accuracy

Rakesh Patel
Rakesh Patel
February, 13 2024
Techniques for improving the accuracy of AI Transcription model

Have you ever struggled to correctly translate an audio recording into text? Transcribing voice into text has never been simpler thanks to advances in artificial intelligence.

Artificial intelligence-based transcription models are algorithms that turn speech into text. They are frequently utilized in many different applications, including speech-to-text programs, captioning, and dictation software.

AI transcription models, however, are not flawless and frequently make mistakes. Accuracy is essential for these models since misunderstanding and misinterpretation might result from this.

That’s why, in this blog, we will explain AI transcription techniques for improving the accuracy of AI transcription models.

Data Pre-processing

Data Cleaning

Data cleaning is a crucial step in raising transcription model accuracy using AI. This entails purging any unnecessary or noisy data from the dataset that will be used to train the model. The performance of the model might be significantly impacted by irrelevant or noisy data, resulting in reduced accuracy.

For instance, background noise like music or traffic in the dataset might confuse the model and lead to erroneous results.

Rotation, Scaling, and Cropping

Rotation, scaling, and cropping are a few of the techniques that may be used to create new data samples from the current data. By doing so, the dataset is enhanced, and the model is strengthened against various speech variances.

For instance, the model is more likely to generalize effectively to new voice inputs if it is trained on a wide dataset that contains many dialects and speaking styles.

Choosing the Right Model Architecture

Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and Transformer models are a few examples of model architectures that may be applied to AI transcription models. The best architecture for the task at hand must be chosen because each of these designs has strengths and limitations of its own.

For instance, RNNs are frequently employed in AI transcription models because they are effective for sequential input, such as voice.

The use of CNNs for transcription models is uncommon since they are more suitable for image recognition applications.

Transformer models, which have a more modern architecture, have grown in favor recently and have been demonstrated to be effective on a variety of tasks, including transcription.

Training on a Large and Diverse Dataset

Following the best practices of AI transcription the accuracy can be increased in part by training the model on a wide and varied dataset. The more data the model gets to learn from, the better it performs. This is true regardless of the dataset’s size.

A diversified dataset also aids in the model’s improved adaptation to various speaking voice inflections and accents.

When transcribing speech from a speaker with an accent that is not represented in the training data, for instance, the model is more likely to deliver correct results if it was trained on a dataset that covers a wide range of accents and speaking styles.

Fine-Tuning the Hyperparameters

A critical step in enhancing an AI transcription model’s accuracy is fine-tuning the hyperparameters. The parameters known as hyperparameters determine how the model performs and behaves.

Aspects like learning pace, number of hidden layers, and dropout rate are within its control. By enabling the model to comprehend the data it is processing better, fine-tuning these hyperparameters can help the model be more accurate.

Grid Search, Random Search, and Bayesian Optimization are a few popular methods for fine-tuning hyperparameters. While Random Search is a more random method to hyperparameter optimization, Grid Search entails experimenting with various combinations of hyperparameters. On the other hand, Bayesian Optimization employs statistical models to direct the search process, increasing its efficiency.

For instance, you may experiment with various combinations of learning rates, hidden layer counts, and dropout rates while fine-tuning the hyperparameters of an AI transcription model. Starting with a narrow range of values for each hyperparameter and progressively expanding it as you get more insight into the behavior of the model.

Data Augmentation

Another method for raising an AI transcription model’s accuracy is data augmentation. By applying random changes to the current data, the training data set is fictitiously enlarged. By learning from additional instances, the model is better able to generalize to fresh, unexplored data.

Flipping, cropping, and adding noise to the data are a few frequent data augmentation methods.

For instance, to create a different version of the data, you could flip the audio file horizontally. Alternatively, you might add some random noise to the data to make it harder for the model to process. By contributing to the data in this way, you are enabling the model to draw knowledge from a larger range of examples, enhancing its robustness, and enabling it to generalize to new data.

Utilizing Transfer Learning

Transfer learning is a method for training new models by utilizing previously taught models as a starting point. As a result of the pre-trained model having previously picked up on many of the characteristics and patterns in the data, this can boost the new model’s accuracy.

For instance, using a smaller, more focused dataset, you might fine-tune a pre-trained model that has previously been trained on a huge dataset. This can speed up and improve the model’s ability to learn from the smaller, more niche data set compared to training it from start.

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Frequently Asked Questions

What are the challenges to improving the accuracy of AI transcription models?

The lack of high-quality training data is one of the main obstacles to enhancing the accuracy of AI transcription models. Additionally, it may be challenging to reach high levels of accuracy due to the wide variety of accents, background noise, and speech patterns.

How does utilizing different audio formats help improve the accuracy of AI transcription models?

Because multiple audio codecs may capture different features of the audio, using those can help AI transcription models become more accurate. For instance, incorporating both stereo and mono audio can assist in teaching the model how to manage various audio inputs.

What effect does the length of audio recordings have on the accuracy of AI transcription models?

The accuracy of AI transcription models can be significantly impacted by the length of audio recordings. Less variety in accents, background noise, and speech patterns may be present in shorter audio files, which makes it more difficult for the model to generalize to new data.

Longer audio recordings may also be more challenging to analyze, which might slow down training and reduce accuracy.

How can the choice of an AI framework affect the accuracy of AI transcription models?

The effectiveness of AI transcription models can be greatly affected by the framework that is used. Different frameworks have various advantages and disadvantages, and some are more appropriate for particular sorts of issues than others.

For instance, certain frameworks could be better adapted to handle background noise than others, while others might be tuned for voice recognition.

Wrapping Up

In conclusion, enhancing an AI transcription model’s accuracy needs a variety of methods, including data cleaning, data augmentation, fine-tuning hyperparameters, and utilizing transfer learning. The accuracy of AI transcription models may be greatly increased with the proper strategy, increasing their reliability and use for a variety of applications.

At, our team of professionals is committed to providing our clients with high-quality solutions and has considerable expertise in creating and optimizing AI models. We would be delighted to hear from you if you’re wanting to improve the accuracy of your AI transcription model or if you require assistance with any other software development requirements.