Built-in Compiler (supporting Python, Java, and C++)
Intelligent Responses
Transcribed Audio
Lexicon, StoryLang, Mocktalk, Lingobo, InstaSpeak are the best paid / free transcription practice tools.
Transcription practice refers to the process of manually converting speech or audio recordings into written text for the purpose of training and improving automatic speech recognition (ASR) systems. This practice involves human transcribers listening to audio samples and accurately typing out what they hear, creating labeled datasets that can be used to train AI models to better recognize and transcribe speech.
Core Features
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Price
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How to use
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Mocktalk | Built-in Compiler (supporting Python, Java, and C++) | Prepare for job interviews independently with AI. Master the job interview process with real-time voice recognition, speech transcription, and intelligent responses. Practice for job interviews with the top companies. | |
InstaSpeak | Automated speaking tests | To use InstaSpeak, teachers can create and send automated speaking tests to their students through the platform. Students can then take the tests anytime and anywhere, instantly receiving detailed analysis and feedback on their grammar, filler words, words pacing, and advanced vocabulary usage. Progress of all the students can be viewed on a central dashboard. | |
Lingobo | Lingobo's core features include: | To use Lingobo, simply sign up for an account on the website, choose your desired level of English proficiency, and start accessing the micro-lessons. Each lesson will involve interactive conversations with the AI, allowing you to practice your speaking and listening skills. | |
StoryLang | Generate stories in your target language |
Starter $1.5 1 credit
| Improve your language learning by reading and listening to stories. Generate stories according to your preferences and start learning |
Lexicon | AI-powered speaking practice |
1:1 Tutor Session $10 Enjoy a fixed-rate 1-on-1 tutor session for personalized teaching.
| Practice speaking with teachers, receive AI-transcribed dialogue, and learn specific words. Use personalized flashcards for enhanced language proficiency. |
Improving virtual assistants and voice-controlled devices
Developing real-time captioning systems for live events or broadcasts
Creating transcripts of meetings, lectures, or court proceedings
Analyzing customer service calls for quality assurance and training purposes
Facilitating the creation of subtitles for videos to improve accessibility
Users have generally praised the improvements in ASR accuracy and performance that have resulted from transcription practice. Many have noted that voice-controlled devices and services are now more reliable and responsive, with fewer errors in transcription. Some have also appreciated the increased accessibility of content through improved automatic captioning and subtitling. However, some users have raised concerns about the privacy implications of using human transcribers and the potential for biased datasets if not properly managed.
A user dictates a message to their smartphone's virtual assistant, which accurately transcribes the speech to text.
A student uses an ASR-powered transcription service to automatically generate captions for a lecture recording.
A journalist employs an ASR tool to quickly transcribe an interview, saving time and effort compared to manual transcription.
To engage in transcription practice, a human transcriber listens to an audio recording and types out the speech they hear as accurately as possible. This process is typically done using specialized software that allows the transcriber to control playback and easily input the text. The resulting text is then paired with the original audio to create a labeled dataset. Multiple transcribers may work on the same audio to ensure accuracy and account for variations in hearing and interpretation.
Improved accuracy of ASR systems through exposure to a wide variety of speech patterns, accents, and vocabularies
Creation of large, diverse datasets that can be used to train more robust AI models
Identification and correction of errors or biases in ASR systems
Enabling the development of ASR for low-resource languages or specialized domains