3 Ways Speech Recognition Training is Changing Machine Learning

Speech is an incredibly powerful thing in our lives, both personally and professionally. With so many widespread use cases tied to our smartphones, computers and other connected devices, the use of speech recognition has brought an even deeper layer of hands-free and voice-based operation to the table. And, speech-based devices like the Google Home, Amazon Echo, and Apple HomePod have brought voice assistance and voice operation into the modern home. What makes this type of AI machine learning successful, though?

Speech recognition training is advancing the ways we use, develop, and train the AI models that are built into these everyday devices. The real impact of this machine learning intensifies every day with the utilization of experts to achieve the highest possible quality of information. Perfect for companies that need to identify and annotate datasets properly to help train AI systems, speech recognition training services ensure that your AI model understands those unique nuances present in your recorded audio data.

Machine learning still has a long way to go to achieve true perfection in many cases, but speech recognition training is helping to ramp up the process in a few key ways:

1. Providing More Accurate Datasets

No two human vocal or speech patterns are the same. With everyone having a unique voice, paired with other variations that can cause more distinct tracts, the diversity of speech can often cause problems for the data that feeds machine learning. No matter how well speech recognition software is programmed to overcome these obstacles, it can never entirely cover the many nuances present in human speech.

Even if an algorithm is made perfectly well for a specific task, if its been trained using poor data, it won’t function properly, won’t come to the right conclusions, and won’t learn properly in order to evolve. This is exactly why speech recognition training exists: to provide the most accurate data possible. A recent study from Oxford Economics and ServiceNow, 51% of CIOs cite data quality as a substantial barrier to their company’s adoption of machine learning. In order to properly train a speech recognition system, you have to throw a lot of quality information its way – this requires the assistance of human-created data to make it the best it can be. With the most accurate data on-hand, machine learning processes can continue to rapidly evolve in the most effective way.

2. Creating More Helpful Voice Assistance

With high-quality speech recognition training comes more accurate, and ultimately, helpful AI voice assistance. When training your automatic speech recognition (ASR) system, you need incredibly large volumes of high-quality data to ensure that your system can understand human speech in a variety of environments. With huge amounts of good data available to train speech recognition systems, voice assistants can better hear commands, complete actions, and understand other contextual information.

We work with Voicero to train speech recognition engines with powerful human transcription and human-assisted speech data. The result is  Eva, an intelligent AI virtual assistant for professional meetings that helps to capture every important detail and action item. With expertly-trained speech recognition systems, Eva and other virtual, voice-based assistance programs can solve problems faster and make your business even more productive.

3.  Supporting More Languages

While having more than enough speech data is a great start, the process of training AI models can only go so far if only one target language is utilized. To use machine learning to its fullest extent, taking advantage of accurate speech recognition training that can manage multiple languages is vital. Having human-assisted speech recognition is the best way to do this, as real live experts can take the time to truly hear what’s being said in every language. By understanding, digesting, and breaking down the nuances of language in a way that speech recognition training systems can work with, AI models quickly become more useful to speakers of different or multiple languages.

At TranscribeMe, we are specialists in transforming large volumes of speech data into client-specific corpora which are used to train Artificial Intelligence (AI) systems and speech recognition (ASR) platforms. These services are currently available in all English accents, Spanish (European & Latin American), Portuguese, Mandarin, Cantonese, Japanese, French and Italian.

We deliver highly accurate, human-verified transcription services which are used to train high accuracy speech recognition output for a wide range of use cases. Our automated speech recognition models are constantly improving – with each file transcribed, our powerful platform learns something new and is able to generate better results. We offer fully-customized AI model training for your speech recognition systems, which includes:

  • Custom annotations
  • Complete full verbatim transcription
  • A multiple step review process
  • Capabilities to include customized meta-tags
  • Multiple language support
  • And much more…

Are you looking to improve the quality of your training data? Human-annotated data is the key to successful machine learning. Contact us and learn more about our powerful automatic speech recognition solutions

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