AI Interview Practice "MENREN" Updates to Suppress "Mishearing" and "Erroneous Generation During Silence" in Speech Recognition - Addressing Generative AI Service Quality Issues with a Unique Approach
X-HACK Co., Ltd. has updated the speech recognition pipeline for its AI interview practice app "MENREN." This update significantly improves the accuracy of speech recognition and the naturalness of the practice experience by correcting homophones according to interview context and removing unnatural text generated during silence, demonstrating the importance of domain-specific layers in generative AI services.
📋 Article Processing Timeline
- 📰 Published: April 21, 2026 at 19:13
- 🔍 Collected: April 27, 2026 at 18:02 (142h 48m after Published)
- 🤖 AI Analyzed: April 27, 2026 at 18:35 (33 min after Collected)
Voice function updated, further improving specialty and accuracy.
X-HACK Co., Ltd. (Headquarters: Shinagawa-ku, Tokyo, Representative Director: Shinsuke Matsuda, hereinafter referred to as "the Company"), which provides "MENREN," an AI interview practice web application specializing in engineer job changes, announces that it has distributed an update to its speech recognition pipeline incorporating AI that understands the context of engineer interviews.
We have successfully incorporated the following into the output of general-purpose speech recognition AI:
- (1) A layer that corrects homophones to appropriate Kanji characters according to the interview context.
- (2) A mechanism that automatically detects and removes unnatural text generated during silence.
This allows users to experience more natural and accurate voice-based interview practice.
Service URL: https://menren.recruit-hub.ai/
## "Domain-Specific Layer" that Determines the Value of Generative AI Services
With the advent of generative AI provided by OpenAI, Anthropic, Google, and others, the number of B2C services centered on AI has rapidly increased in recent years.
In this environment, whether a "specialized layer" that optimizes the output of general-purpose AI for a company's own service domain can be inserted, rather than using the output as is, has become a crucial point determining the difference in service experience.
Especially in services that handle voice, there are two optimization points for the output of general-purpose speech recognition AI:
- Selection of homophones that understand the context: A layer that converts to appropriate Kanji characters according to industry-specific vocabulary and the speaker's context.
- Stable operation against silent input: A mechanism that detects and removes meaningless text (e.g., "Thank you for watching") that speech recognition AI may generate during silence or background music.
MENREN, as an AI interview coach specializing in the field of engineer job changes, has implemented these two optimizations.
## Two Mechanisms to Make Speech Recognition "More Natural"
MENREN is a service where users respond to interview questions into a microphone, and an AI interviewer scores and provides feedback on 5 axes for engineer recruitment (technical explanation ability / logical structure / specificity / expressiveness / fluency). The foundation of the experience is "the AI correctly understanding what the user has said." With this update, we have newly installed the following two specialized layers on top of the general-purpose speech recognition AI.
### ① Automatic selection of homophones that understand the context
This is a mechanism where AI, understanding the context of the interview, automatically selects the appropriate Kanji characters for homophones in the speech recognition result. Speaking habits and fillers are retained as they are, respecting the user's utterances.
### ② Immediate feedback for recording troubles through silence detection
If the microphone is muted or silence occurs, speech recognition AI may generate fixed phrases derived from training data (e.g., "Thank you for watching"). This mechanism automatically detects this and immediately notifies the user with "Audio could not be detected." This allows users to notice recording troubles on the spot and provides a reliable practice experience.
## Value Provided by This Update
- Improved scoring accuracy: More accurate feedback due to precise transcription aligned with the context.
- Immediate detection of recording troubles: Users can notice recording errors on the spot, providing a reliable practice experience.
- Reliability supporting continuous use: High trust in the results promotes repeated practice.
In services centered on generative AI, a design that inserts a specialized layer optimized for a company's domain into the output of a general-purpose model creates a difference in service experience. MENREN will continue to refine the technology of this "specialized layer" in the specialized field of engineer job changes.
## Company Profile
Company Name: X-HACK Co., Ltd.
Location: THE CASK GOTANDA 702, 2-5-2 Higashi-Gotanda, Shinagawa-ku, Tokyo
Representative: Shinsuke Matsuda, Representative Director
Established: March 2018
Business Activities: Generative AI/LLM utilization support, design and development of AI-driven development platforms, IT system implementation support and contract development.
Corporate Site: https://x-hack.jp
## Contact for this matter
X-HACK Co., Ltd.
Contact: Toyoda
E-mail: support@menren.recruit-hub.ai
X-HACK Co., Ltd. (Headquarters: Shinagawa-ku, Tokyo, Representative Director: Shinsuke Matsuda, hereinafter referred to as "the Company"), which provides "MENREN," an AI interview practice web application specializing in engineer job changes, announces that it has distributed an update to its speech recognition pipeline incorporating AI that understands the context of engineer interviews.
We have successfully incorporated the following into the output of general-purpose speech recognition AI:
- (1) A layer that corrects homophones to appropriate Kanji characters according to the interview context.
- (2) A mechanism that automatically detects and removes unnatural text generated during silence.
This allows users to experience more natural and accurate voice-based interview practice.
Service URL: https://menren.recruit-hub.ai/
## "Domain-Specific Layer" that Determines the Value of Generative AI Services
With the advent of generative AI provided by OpenAI, Anthropic, Google, and others, the number of B2C services centered on AI has rapidly increased in recent years.
In this environment, whether a "specialized layer" that optimizes the output of general-purpose AI for a company's own service domain can be inserted, rather than using the output as is, has become a crucial point determining the difference in service experience.
Especially in services that handle voice, there are two optimization points for the output of general-purpose speech recognition AI:
- Selection of homophones that understand the context: A layer that converts to appropriate Kanji characters according to industry-specific vocabulary and the speaker's context.
- Stable operation against silent input: A mechanism that detects and removes meaningless text (e.g., "Thank you for watching") that speech recognition AI may generate during silence or background music.
MENREN, as an AI interview coach specializing in the field of engineer job changes, has implemented these two optimizations.
## Two Mechanisms to Make Speech Recognition "More Natural"
MENREN is a service where users respond to interview questions into a microphone, and an AI interviewer scores and provides feedback on 5 axes for engineer recruitment (technical explanation ability / logical structure / specificity / expressiveness / fluency). The foundation of the experience is "the AI correctly understanding what the user has said." With this update, we have newly installed the following two specialized layers on top of the general-purpose speech recognition AI.
### ① Automatic selection of homophones that understand the context
This is a mechanism where AI, understanding the context of the interview, automatically selects the appropriate Kanji characters for homophones in the speech recognition result. Speaking habits and fillers are retained as they are, respecting the user's utterances.
### ② Immediate feedback for recording troubles through silence detection
If the microphone is muted or silence occurs, speech recognition AI may generate fixed phrases derived from training data (e.g., "Thank you for watching"). This mechanism automatically detects this and immediately notifies the user with "Audio could not be detected." This allows users to notice recording troubles on the spot and provides a reliable practice experience.
## Value Provided by This Update
- Improved scoring accuracy: More accurate feedback due to precise transcription aligned with the context.
- Immediate detection of recording troubles: Users can notice recording errors on the spot, providing a reliable practice experience.
- Reliability supporting continuous use: High trust in the results promotes repeated practice.
In services centered on generative AI, a design that inserts a specialized layer optimized for a company's domain into the output of a general-purpose model creates a difference in service experience. MENREN will continue to refine the technology of this "specialized layer" in the specialized field of engineer job changes.
## Company Profile
Company Name: X-HACK Co., Ltd.
Location: THE CASK GOTANDA 702, 2-5-2 Higashi-Gotanda, Shinagawa-ku, Tokyo
Representative: Shinsuke Matsuda, Representative Director
Established: March 2018
Business Activities: Generative AI/LLM utilization support, design and development of AI-driven development platforms, IT system implementation support and contract development.
Corporate Site: https://x-hack.jp
## Contact for this matter
X-HACK Co., Ltd.
Contact: Toyoda
E-mail: support@menren.recruit-hub.ai