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Conventional speech recognition systems could only understand limited words or simple instructions. However, the evolution of AI (Artificial Intelligence) technology, particularly the introduction of deep learning, has dramatically improved accuracy, and in recent years, it has become possible to recognize natural conversation and complex utterances with high precision.
However, some may be postponing the introduction due to questions such as "How exactly does speech recognition work?" or "In what business situations can it be utilized?"
In this article, in addition to the basics such as the mechanism and main functions of speech recognition, we will explain the benefits of utilization and the latest use cases in detail.
In the latter half of the article, we also introduce the steps for developing speech recognition AI and points to note during implementation, so the content is designed to help you understand the flow up to the start of using speech recognition and be useful for actual utilization.
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【Table of Contents】 |
1. What is Speech Recognition?
Speech recognition is a technology where a computer analyzes speech uttered by humans and converts that content into text data. It was quickly recognized in daily life through voice input functions on smartphones such as Siri and Google Assistant.
In recent years, utilization using generative AI such as ChatGPT has been progressing, and new ways of use can be seen such as giving instructions to AI through voice, dialogue, transcription from conversation to text, and summarization.
In this way, through coordination with generative AI, speech recognition technology has moved beyond the simple framework of voice input and continues to develop as a more advanced and flexible interface.
Mechanism
Speech recognition works by being recognized through the following four steps.
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The input speech is first converted into digital data. Then, features used for AI learning are extracted.
In the latest deep learning models, technologies that learn directly from raw speech waveforms like wav2vec have also appeared, and in some cases, the conventional feature extraction process can be omitted.
Next, from the extracted features, the model identifies "phonemes," which are the smallest units of language such as vowels and consonants. It is possible to identify phonemes with high precision while maintaining the continuity of the speech.
Subsequently, the pronunciation dictionary (a database for linking phonemes and words) determines which word the identified phoneme corresponds to.
Finally, based on statistical information learned from massive amounts of text data, it outputs natural sentences while considering the relationship between words. In recent years, the use of Transformer-based LLMs (Large Language Models) such as BERT and GPT has been increasing.
Due to the evolution of deep learning technology, the above processes that were conventionally processed individually are being integrated. Furthermore, with the appearance of end-to-end models, direct conversion from speech to text has become possible, contributing to further precision improvement in speech recognition technology.
Usage Scenes
Speech recognition is utilized in a wide range of fields from daily life to business. Below are some typical usage scenes.
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Along with the development of deep learning technology, further application in various fields is expected in the future.
2. Benefits of Utilizing AI-Based Speech Recognition
Speech recognition technology utilizing AI contributes significantly through improvements in operational efficiency and the sophistication of provided services. Below, we introduce the benefits of utilizing AI-based speech recognition.
Operational Efficiency
By utilizing speech recognition, conventional manual input becomes unnecessary, and tasks such as data entry and document creation are significantly accelerated. For example, in the creation of reports and customer interaction logs, a large amount of information can be recorded in a short time, which reduces daily operational burden and improves productivity.
Also, by utilizing speech recognition, errors caused by manual input can be reduced. This makes the entire series of processes from document creation to approval efficient, and reduces the time spent on checking and correcting content.
Speech recognition technology is effective even in situations where massive text input is required, such as customer support and meeting minutes creation. Since uttered content can be recorded in real-time, prompt response becomes possible and immediate information sharing is realized.
Improvement of Accessibility
Speech recognition technology provides an environment where even people with visual impairments or hand disabilities can operate devices and apps through voice. Various operations of smartphones and PCs become possible with just the voice, significantly improving accessibility.
Therefore, by using speech recognition, services that are easy to use for diverse users can be provided, leading to improved customer satisfaction.
Real-Time Information Gathering
Speech recognition technology can analyze voice data in real-time and instantly extract important insights.
For example, by utilizing speech recognition in customer support or sales settings, needs or complaints uttered by customers can be grasped early, enabling quick response. Since prompt action can be taken when problems occur, it leads to preventing a decline in customer satisfaction and the expansion of complaints.
Also, insights obtained from voice data are useful for sentiment analysis to understand customer expectations and interests. Speech recognition serves as a means to strengthen indirect communication with customers and promote acceleration of decision-making in business.
3. Use Cases for Speech Recognition
Speech recognition is being utilized in various business scenes, such as creating meeting minutes and transcribing interview data. Below, we introduce use cases for speech recognition.
Halving Minutes Creation Time (Imuraya)
At the Imuraya Group, the challenge was that creating meeting minutes took nearly a week. With conventional methods, it took time because it was necessary to search for specific parts of the meeting recording or repeatedly listen back to the same part.
Therefore, by introducing a speech recognition AI system developed by Rimo LLC, minutes creation became possible at double the conventional speed, and work time was significantly reduced. Through this, employees were freed from the burden of minutes creation, leading to improved operational efficiency as well as stress reduction.
Reference: https://rimo.app/case-studies/Z24aLo4BOZW2yZ8QzC8d
Establishing a System for Easy Dialogue for the Hearing Impaired (Toride City)
In Toride City, Ibaraki Prefecture, where the aging rate exceeds 30%, the challenge was to establish an environment where hearing-impaired people or those hard of hearing could communicate easily.
Therefore, the Toride City Council Secretariat decided to utilize a speech recognition AI system developed by Advanced Media, Inc. The introduced speech recognition system has a caption pop-up function that displays text on a separate screen, working as a mechanism to display conversation content as captions in real-time.
Through this, it has become possible to provide clear and easy-to-understand explanations to the various people who visit for consultation.
Reference: https://voxt-one.advanced-media.co.jp/case/5307/
4. How to Build a Speech Recognition AI Model
A reliable speech recognition AI model can be developed by following several steps. Below, we introduce how to build a speech recognition AI model.
Clarification of Introduction Purpose and Requirement Definition
First, it is important to clarify the purpose of introduction and define specific requirements. The key is to grasp the following:
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Processing speed is often a trade-off with the precision and the number of language types mentioned earlier. Also, setting a budget and introduction schedule is important for the implementation.
Data Collection and Preprocessing
To increase the precision of speech recognition models, high-quality data collection and accurate data preprocessing are required. For speech recognition, a more precise model can be built by utilizing diverse utterance patterns.
Collected voice data requires preprocessing such as the following:
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Through the above processing, consistency of speech signals is maintained and the model becomes able to accurately recognize speech. Also, preprocessing to convert data into a format suitable for the AI model to be used is common.
Annotation
To build speech recognition models or improve precision, it is indispensable to apply accurate annotation to the collected voice data.
In annotation, voice data is first accurately transcribed, and if necessary, labeling at the phoneme level is performed. Labeling at the phoneme level is useful for breaking down speech into fine pieces and enabling the model to recognize pronunciation units.
Accurate annotation leads directly to the quality of training data used for model learning, and by extension, the precision of speech recognition models.
5. Building an AI Model
For AI speech recognition models, there are several architectures that can be considered standard as follows, and it is necessary to choose according to their respective characteristics.
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The above models are built and trained using massive amounts of data.
After training, model performance is evaluated, and improvement measures such as hyperparameter adjustment and data addition are performed for precision improvement as necessary.
6. Points to Note When Building a Speech Recognition AI System
When building a practical speech recognition AI system, there are several points to note regarding data handling and more. Below, we introduce matters that require particular attention.
Consider Data Privacy
Because user voice data is collected when introducing speech recognition technology, personal information protection is indispensable. It is necessary to appropriately manage storage methods and access rights, and confirm whether anonymization processing is applied.
Also, if performing data encryption or obtaining explicit consent from users, compliance with privacy laws including the Act on the Protection of Personal Information should also be considered. Through these measures, a highly reliable speech recognition system can be provided while protecting users' personal information.
Prepare High-Quality Voice Data
For building high-precision speech recognition models, it is important to use high-quality voice data for training. This is because if training data is insufficient or biased, the model will be biased, potentially causing misrecognition. Please also see this article regarding points to note on data collection.
"Things to keep in mind when requesting annotation data collection"
In particular, recognition tends to become difficult for specific accents or voice characteristics. If necessary based on the purpose of utilization, a model capable of handling a wide range of users can be built by preparing a balanced dataset that includes dialects or unique expressions.
Risk of Misrecognition
Even with high-precision speech recognition technology, risks of misrecognition accompany it due to various causes such as those listed below.
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To reduce the risk of misrecognition in speech recognition, interfaces where users can immediately check and correct speech recognition results or the introduction of multiple check steps are effective.
Also, in particularly important markets, developing specialized speech recognition models and optimizing them according to market characteristics enables high-precision recognition matched to the market's linguistic characteristics.
7. Summary
Speech recognition technology has become indispensable in many business scenes to improve customer experience and operational efficiency. It is utilized in various applications such as smoothing communication with customers, real-time information collection, and automatic creation of meeting minutes, enhancing the speed and precision of operations.
Also, in the modern era where work-style reform is promoted, it is an important tool for reducing employees' operational burden and improving operational efficiency and productivity. Through the introduction of speech recognition technology, daily operations are processed quickly and an environment where employees can concentrate on more important tasks is established.
Going forward, speech recognition technology will continue to develop as a high-value tool for companies while meeting more diverse needs.
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