I bet anyone in our modern digitalized landscape came across an audio or video in another language and was curious to understand what was said. In this case, it was necessary to convert the speech to text and then translate it. The process of representing spoken language in written form is called transcription. Transcription services accordingly are professional services used for transcribing recorded speech to typewritten texts. These services can be performed manually by human transcriptionists or through automated speech recognition (ASR) technologies, or a mix of these methods.
With the rapid improvement of ASR, the question arises: will the transcriptionist job die out soon? Let’s break down transcription services in our digital era.
Human VS Automated Transcription Services
Human Transcription Services: These services involve skilled human transcriptionists who listen to audio or video recordings and transcribe the content into written text. They are trained to handle various accents, dialects, and complex audio recordings. Their ability is well suited to capture certain shades, tunes and contexts which are not easily recognized by the programmed systems. In most instances of legal, medical, educational, press, and trade applications it is commonplace to use human transcription services.
Automated Transcription Services: Automated transcription services utilize speech recognition technology to transcribe spoken content. They offer quicker and cheaper services as compared to human ones. Nevertheless, their accuracy could dwindle significantly when it comes to unclear audio recordings or some types of accents.
Challenges for Automated Speech Recognition
Automated Speech Recognition faces several main challenges:
Accents and Dialects:
Some ASR systems may not effectively convert speech with various dialects and accents resulting in recognition errors. It’s because the machines trained on limited data for a specific accent may not have sufficient exposure to that accent’s characteristics. Moreover, some accents include unique vocabulary or colloquialisms that may not be present in standard language models used by the systems.
Variability in Speech:
Some speakers may talk too fast or slow, use varied pitch variations, and pronounce words differently, posing problems for an ASR.
Ambiguity:
Such systems may encounter problems of homophones (words which sound alike but have different meanings), and/or ambiguity of words in the context.
Lack of Context:
Machines often lack contextual understanding, making it challenging to accurately interpret and transcribe spoken language. That is especially true when it comes to complicated conversations.
Out-of-Vocabulary Words:
Automated tools may struggle with words or phrases not present in their training data. For example, some specialized language and names.
Cross-Linguistic Issues:
The ASR system may face problems while transitioning or switching from one language/dialect to another within the same conversation.
Speaker Independence:
Some ASR systems need customisation to perform at their best levels with different speakers.
Real-time Processing:
The accomplishment of a low-latency real-time transcription may be difficult in long dialogues or big applications.
Data Privacy:
Due to the nature of processing private and personal conversations through ASR systems, it is imperative to implement reliable data protection measures in the face of privacy queries.
Background noise:
It interferes greatly with ASR systems’ accuracy. They still struggle to distinguish between the intended speech and unwanted background sounds. Let’s analyse why.
Background Noise as One of the Main Difficulties for ASR
Well, to get the best-quality transcript you should obviously obtain the best recording possible. One must admit, that the clearest audio can only be recorded in a specially equipped room named an ‘audio recording studio’. However, real-life situations in which you may need to record someone’s voice differ greatly from the conditions inside such a studio.
I bet you heard an awful loud sound when talking over the phone to a subscriber who was outside in the strong wind. The same thing happens if you record a voice while you are outdoors and not using a windscreen or protective cover for your microphone.
Apart from natural sounds, there is a great variety of background noise, especially in cities.
When recording speech near a busy road or highway, the noise of passing vehicles can interfere with ASR accuracy. This is particularly relevant for hands-free calling systems in cars or for voice assistants used in vehicles. In construction or production sites, equipment can generate loud and continuous background sounds. On public transport, ASR may struggle to transcribe speech precisely due to the noise generated by engines, brakes, and the general hustle and bustle of commuters. In large gatherings there is the collective noise of the crowd.
Even when you are home, noise from household appliances or family members talking in the background can disrupt the accuracy of voice recognition. Moreover, the sounds of pets, doorbells, or sirens outside their homes, can affect the clarity of speech and ASR transcription accordingly.
To address these challenges, automated recognition systems often employ noise reduction techniques, acoustic modelling, and machine learning algorithms to filter out unwanted background noise and improve accuracy. However, in most cases, the noise makes it difficult for machines to provide accurate transcriptions, especially without prior training on such noise patterns.
I remember, our team of professional Russian Language Services providers got an order to transcribe a phone conversation with the sound of a fire alarm. I’d say, it wasn’t even a background noise – the sound was so loud, that it almost fully dominated over the speakers’ voices. That was challenging, but we still managed to decipher the conversation between those Russian people and then translate it into English.
Conclusion
So why do we still have orders? Because human brains are still the most precise tool for speech recognition. And it will definitely remain that in the foreseeable future. Of course, one could resort to automated transcription, which allows one to generate first drafts of transcripts in no time, that can be later edited or reviewed by humans for accuracy.
Russian transcription services are employed in diverse sectors and businesses that depend on precise and trustworthy transcription of verbal communication. So, the transcriptionist’s job will be still in demand where accuracy is key.
