What is AI Voice Recognition? - 3 Examples of Voice Recognition Mechanisms and Applications -

1. The Mechanism of Voice Recognition (Converting Voice to Text in Four Steps)

The audio data inputted by recording devices such as microphones consists of a mixture of various waveforms. While it is easy for our ears to identify human voices among them, it is not a simple task for machines. Even if the corresponding waveforms for words can be identified, machines cannot correctly recognize them and issue accurate instructions unless they are converted into text data. Additionally, it is necessary to correctly select homophones during the text conversion process. To solve these issues, we go through four main steps to convert speech into text. The technologies used in each step are acoustic analysis, acoustic models, language models, and pronunciation dictionaries, which will be introduced next.

1-1. Acoustic Analysis

Acoustic analysis refers to the process of analyzing the features of input audio (such as frequency and volume) and extracting and transforming it into data that can be easily handled by AI. In the first place, AI cannot recognize speech from raw audio data in the same way humans do. For example, amidst various noises in the city, humans can easily recognize the voice of a specific person. However, in the data before acoustic analysis, AI recognizes it as a mixture of various sounds. Therefore, it is necessary to digitize the data so that AI can recognize human speech, and to remove background noise and other disturbances. This process is acoustic analysis. Based on the extracted human speech data, AI advances its speech recognition.

1-2. Acoustic Model

An acoustic model is the process of extracting phonemes by comparing data extracted from acoustic analysis with data previously learned by AI. A phoneme is defined as the smallest unit of meaning when speech is subdivided, and in Japanese, phonemes include vowels, consonants, and nasal sounds. To illustrate with characters, it is like each character in "o, mo, te, na, shi". The training data for phoneme extraction consists of various human voices processed from thousands of people and thousands of hours. By extracting phonemes in this way, the necessary information for AI to convert speech into text is organized.

1-3. Pronunciation Dictionary

Once the phonemes, which are the smallest units of speech, are determined by the acoustic model, it is necessary to reconstruct those phonemes into the correct words. The tool used for this is the pronunciation dictionary. This serves as a database that combines the phonemes extracted by the acoustic model to form words. By concatenating the phonemes using this pronunciation dictionary, it becomes possible to construct a word acoustic model that corresponds to actual words for the first time. In the earlier acoustic model, the phonemes "o, mo, te, na, shi" are combined to form the image of "omotenashi." Once the words are constructed in this way, the next step is to proceed to the language model.

1-4. Language Model

A language model is a representation of the "words" that humans speak or write, modeled by the probability of word occurrence. For example, the phrase "to entertain customers" could also be interpreted as "to not show customers," but the probability of that is low. Recently, language models based on neural networks have become widely used. The GPT-3, which emerged in 2020 as a large-scale language model, uses 175 billion parameters.

 

These technologies enable our conversations to be recognized by AI, converted to text, and even utilized for device operations.

2. Benefits of Utilizing AI Voice Recognition

Utilizing voice recognition technology has various benefits. Here, we will introduce some key points that are particularly noteworthy.

2-1. Streamlining Operations

By implementing voice recognition technology, significant improvements in operational efficiency can be achieved. For example, when creating minutes during meetings or business negotiations, it was traditionally necessary to listen to recorded audio and manually type the notes. However, by utilizing voice recognition technology, audio can be transcribed into text in real-time, greatly reducing the time required for this task. This enables a more efficient allocation of employee resources and is expected to enhance productivity.

2-2. Reduction of Input Errors

When humans input data manually, typographical errors due to human error and mishearing are unavoidable. However, by utilizing speech recognition technology, it is possible to significantly reduce mishearing and input errors. Of course, attention must be paid to background noise and the speaker's volume, but by having humans perform the final check, it is possible to achieve highly accurate transcriptions. As a result, the accuracy of the data improves, and the reliability of the business increases.

2-3. Hands-free input is possible

By using voice recognition technology, hands-free input becomes possible. Since it allows for converting speech to text without using hands, it enables multitasking and is expected to improve work efficiency for those who struggle with typing or spend a lot of time on transcription. Additionally, it allows individuals with physical disabilities to easily input information, contributing to improved accessibility.

2-4. Improvement of Customer Experience

Voice recognition technology is also very useful in the field of customer service. For example, in customer interactions at call centers, converting customers' voices to text in real-time and presenting it to operators enables quick and accurate responses. This is expected to improve customer satisfaction and enhance the company's brand image.

3. Tasks that can be streamlined with AI voice recognition

3-1. Creation of Meeting Minutes

By utilizing AI voice recognition during meetings, you can transcribe spoken words into text in real-time. This significantly reduces the time required to create meeting minutes and improves the accuracy of the content. For example, by using the automatic subtitle feature of meeting applications, it is possible to share the meeting minutes immediately after the meeting ends.

3-2. Customer Support Response

In the call center, we use AI voice recognition to automatically transcribe customer inquiries into text, allowing operators to respond quickly. This reduces response time and leads to increased customer satisfaction.

3-3. Data Entry Work

By utilizing voice recognition, data entry tasks can be made more efficient. This is particularly effective in environments where manual data entry is difficult, such as in medical settings and fieldwork. It is expected to be used when healthcare professionals input patient information using their voice.

3-4. Document Creation

Creating long reports and emails can be done smoothly by utilizing voice recognition. This eliminates the hassle of typing and allows you to quickly turn your ideas into reality. Using the voice input feature of document creation software makes writing more comfortable.

3-5. Translation Services

By combining AI voice recognition and translation functions, real-time translation becomes possible, which is useful in international conferences and situations requiring multilingual support. Using the voice input feature of the translation service facilitates smooth communication between different languages.

3-6. Task Management

You can use voice recognition to add tasks directly to your task management app. This allows you to record tasks the moment you think of them, helping to prevent forgetfulness. Some task management tools also come with features that remind you using voice.

3-7. Business Support through Voice Assistants

By using a voice assistant, you can streamline many of your daily tasks. You can easily perform operations such as checking your schedule, sending emails, and obtaining weather information using voice commands.

3-8. Utilization in Educational Settings

In the educational field, voice recognition can be used to transcribe lecture content into text and provide it to students as notes. This allows students to focus on the lecture and is helpful during review. Tools for transcribing recorded lectures are also becoming widely used.

 

AI voice recognition technology not only streamlines these tasks but also has the potential to transform the way we work. Considering its implementation may improve operational efficiency, so it might be worth considering.

4. Use Cases of AI Voice Recognition

By utilizing AI voice recognition, we can expand services and improve operational efficiency. This time, we will introduce three use cases.

4-1. Case Study 1: Introduction of a Multilingual AI Robot for Station Information Centers

By installing an interactive robot equipped with multilingual voice recognition AI at the information center of the station, we can expect to improve the efficiency of inquiries and customer service for travelers and others.

Furthermore, by understanding user needs from dialogue logs and reflecting them in services, we can provide services that are more in demand by users. Analyzing dialogue logs allows us to collect and analyze customer feedback and satisfaction, which can also be reflected in our services, making it a strength of voice recognition AI. It is expected to bring benefits not only as a simple voice guide but also in improving customer satisfaction and expanding services.
>>Challenges pointed out by the Tokyo Metropolitan Bureau of Transportation regarding the dialogue robot installed near the station ticket gate

4-2. Case Study 2: Business Improvement and Efficiency in Call Centers

The field where voice recognition technology is most widely implemented is call centers. Traditionally, when transcribing call data into text at call centers, a person had to listen to the audio data and transcribe it. With the advancement of voice recognition technology, the accuracy of such AI-driven transcriptions has become very high.

The electronic manual for the call center can amount to thousands of pages when printed, making it quite labor-intensive for operators to search for FAQs. By introducing voice recognition AI to address these challenges, we can expect improvements in operational efficiency and customer satisfaction.
>>Significantly improve call center operations with real-time voice recognition

4-3. Use Case 3: Automation of Meeting Minutes

Until now, meeting minutes had to be recorded in real-time by the person in charge or transcribed while listening to recorded audio data. In real-time, there is a possibility of missing or mishearing information. Additionally, transcription can be a double workload and can take a significant amount of time depending on the content. As the demand for faster operations increases, transcription work can pressure the execution of other tasks and contribute to a decrease in the productivity of the person in charge as a time-consuming routine. By introducing voice recognition AI to address these challenges, business improvements are expected.
>> Streamlining a wide range of transcriptions, from creating meeting minutes to interviews
With operational innovations, recognition accuracy has increased to over 90%.

5. Summary

This time, we introduced the mechanism and benefits of AI-based voice recognition, as well as case studies of its application.

 

Recently, the scope of AI voice recognition is expanding more and more. Along with this, the need for creating training data for AI to learn is also increasing.

 

If you want to reduce the costs of annotation work for creating training data, considering outsourcing or delegating the annotation work is one effective option. Our company offers a wide range of services, from consultations on annotation tools to support in formulating annotation specifications, creating specification documents, and even handling the annotation work itself, so please feel free to reach out to us.