Patent Description:
Real-world speech signal recordings are often corrupted with environmental noises and interfering speakers. Audio source separation or speech enhancement techniques, can potentially be used as an audio pre-processing step to suppress such noises for improved user-experience in a number of applications, including mobile voice communications, conference calls, hearing aids, and other down-stream audio recognition tasks, such as robust automatic speech recognition (ASR). The current wide-spread trend in using speech-aware applications on mobile and IoT devices has also been driving research interests in development of real-time speech enhancement solutions that can run efficiently on-device. It is known from <CIT> Al to perform audio enhancement using a selected model among a plurality of models that have been trained to use speaker-specific embedding (e.g., an "i-vector") information.

Recent advancements in speech enhancement have improved the quality of existing speech enhancement solutions which do not operate in real-time. Typically, these solutions are used to clean collected data for training of other audio tasks. For example, speech enhancement techniques are focussed on enhancing perceptual quality of telephony or the performance of audio related down-stream tasks such as lower word error rate, WER, for automatic speech recognition, ASR.

There is a significant body of work aimed at offline (i.e. non real-time) speech enhancement. Currently, however, on-device deployment of real-time speech enhancement solutions have not been achieved for a variety of resource-constrained devices (such as smartphones and Internet of Things, IoT, devices).

Most work on decreasing model size and/or latency for effective deployment is based on bidirectional architectures (i.e., non-causal), or on couples of inter- and intra- unidirectional architecture components applied in a dual path manner which effectively require access to the entire past, present and future to enhance the present frame (e.g., group communication line of work), and are therefore unsuitable for real-time deployment.

The present applicant has recognised the need for an improved sound enhancement mechanism that overcomes these problems.

The invention is as defined in appended independent claims <NUM>, <NUM>, <NUM> and <NUM>.

Implementations of the present techniques will now be described, by way of example only, with reference to the accompanying drawings, in which:.

Broadly speaking, the present techniques generally relate to a system, computer-implemented method and apparatus for training a machine learning, ML, model to perform sound enhancement for a target user in real-time, and to a method and apparatus for using the trained ML model to perform sound enhancement of audio signals in real-time. Advantageously, the present techniques are suitable for implementation on resource-constrained devices that capture audio signals, such as smartphones and Internet of Things devices.

The term "noisy audio signal" used herein means an audio signal that contains speech of a user and noise, and which is to be enhanced in real-time by a trained ML model.

The term "clean audio samples" used herein means audio signals that only contain speech of individual users, and which are used to train a ML model to perform sound enhancement.

The term "corrupted audio samples" used herein means audio samples that have been generated as part of the process to train a ML model, where the corrupted audio samples are generated by adding at least one noise sample to a clean audio sample.

The term "enhanced audio sample" used herein means the output of the ML model during the training process, where the enhanced audio sample results from using the ML model to enhance a corrupted audio sample.

As will be described in more detail below, the present techniques provide an on-device, real-time sound enhancement approach that is able to extract speech signals of a target user from noisy single-channel audio recordings. A successful deployment of such solutions can improve user-experiences in, for example, telephonic conversation and can improve performance of a downstream automatic speech recognition (ASR) system, especially in noisy conditions. The present techniques combine causal Time-Depth Separable convolution blocks and unidirectional recurrent layers in a novel way to create a Personalized Sound Enhancement Network (PSE-Net) that facilitates a low-latency on-device solution, without degrading the quality of the source extraction. Through a series of evaluation experiments, it is shown that PSE-Net outperforms state-of-the-art solutions in streaming-based sound enhancement, under two types of noise: non-stationary and babble. The impact of sound enhancement is objectively measured by computing word-error-rate in a downstream ASR task, and good transferability of sound-enhancement models across languages is shown.

<FIG> is a schematic diagram of the sound enhancement approach of the present techniques. Generally speaking, a sound enhancement module of the machine learning model may take as inputs (i) a noisy recording that includes a target user's speech, and (ii) a voice profile for the target user, and may provide as an output an enhanced audio signal that only contains the target user's speech. In other words, noise is removed from the input audio signal.

As mentioned above, there has been a recent focus on speech enhancement research aimed to serve as a pre-processing component to enhance perceptual quality of telephony or the performance of audio related down-stream tasks such as lower WER for ASR.

Though there is a significant body of work aimed at off-line speech enhancement, the pervasive use of audio based applications on mobile and IoT devices is driving research into novel directions to allow for real-time speech enhancement.

Representing the best perceptual and WER quality, but at the cost of model size and real-time, recent work provides significant milestones in improved performance from using bidirectional RNNs operating on the frequency domain to non-causal non-grouped convolutions with non-efficient non-causal self-attention models operating on the time domain.

Improvements based on operating at the time rather than frequency domain at the cost of computational budget are expected, as phase information is used implicitly in the former but not in the latter. Also the use of conformer-like architectures naturally improves enhancement quality, but at the expense of real-time factor and model size requirements.

The present techniques differ from this line of research, as the present techniques operate on frequency-time without any look-ahead, introduce casual grouped convolutions to speech enhancement literature, and leverage RNNs as a novel mechanism to promote smaller model sizes. Operating on the frequency domain allows for a more efficient integration with ASR models, giving the opportunity to avoid duplicated effort in converting from frequency- to time- domain between speech enhancement and ASR models. Nevertheless, the ideas presented here could also serve to model time domain signals, by increasing convolution dilations.

On the other hand, models suitable for real-time (or near real-time with a small look-ahead) represent only a fraction of recent work and have mostly been formed of unidirectional RNNs such as the seminal work. The present techniques differ from these developments by leveraging grouped convolutions to decrease the number of model parameters, as well as by the use of unidirectional RNNs to allow further reduction of the filter sizes in the convolutions, thereby further decreasing model size compared to what would otherwise be possible.

While there is already significant recent work on this front there is still a considerable gap in performance when compared with non-real-time speech enhancement models, indicating perhaps that there is a wide possibility for improvement. Great strides have recently been made in the miniaturizations of speech enhancement models, but at the cost of real-time solutions or quality.

The present techniques differ from these efforts by preserving enhancement quality in a way that is comparable with non-real time solutions. This is achieved even though the ML model of the present techniques does not require any look-ahead. Furthermore, the ML model yields a small enough model footprint that makes the present techniques suitable for implementation on resource-constrained devices (such as mid-range or high-end smartphones).

The present techniques provide a real-time personalized sound enhancement approach that extracts the speech signal of a target user from a noisy single-channel audio recordings on mobile and wearable devices. A successful on-device deployment of a personalized sound enhancement has important product implications, including:.

improved audio call quality, where user's noisy data is cleaned on-device and then transmitted to the far-end listener; and.

improved usability of services, like automatic speech recognition (ASR), by decreasing the word-error-rate under noisy conditions.

Lately, deep learning techniques have been successfully applied in speech enhancement tasks, e.g., to solve the well-known blind-source separation problem. However, a successful solution to the blind source separation problem needs to address two main challenges: (i) identify the total number of speakers or sound sources present in a recorded audio, and (ii) be invariant to the permutation of the identified source-labels during training. Interestingly, sound enhancement on personal devices can often be reduced to the problem of sound extraction, where the identity of the target or source speaker is known a priori, e.g., the owner of the device.

The present techniques focus on sound enhancement, where the main objective is to extract audio pertaining to the speech of a target user. The sound enhancement may be personalised or non-personalised, as mentioned above, and the same ML model may be able to switch between performing personalised and non-personalised sound enhancement. The personalisation may be performed by conditioning the output of a source extractor network on a voice profile of the target user, represented by a fixed-size embedding vector. To measure the impact of the sound enhancement technique, ASR is considered to be the downstream task. Despite best efforts, ASR performance remains poor in noisy and crowded environments and personalised sound enhancement could potentially increase ASR robustness in many applications including personal assistants, car navigation, robots, and medical dictation devices.

In spite of the progress made in sound extraction methodologies, there exists a considerable gap in the performance of real-time streaming sound enhancement solutions, compared to their non-causal alternatives. To bridge this gap, the present Applicant explores the use of causal Time Depth Separable (TDS) convolution blocks with unidirectional RNNs to form an encoder-decoder architecture that (a) operates in streaming mode, (b) shows superior Signal-to-Distortion Ratio (SDR), and (c) achieves lower word-error-rate (WER) when applied to a downstream ASR task.

The present techniques address the following main technical challenges:.

Suppresses both background chatter and non-stationary ambient noise, while keeping the target speaker's audio intact.

Does not degrade spectrogram representation of the clean input audio after enhancement, i.e., almost no changes are made to clean input audio.

Supports real-time operations, i.e., the proposed solution operates in streaming mode and has a small latency.

Performs causal prediction, i.e., it only considers past information while predicting denoising input audio recordings.

The present techniques combine causal Time-Depth Separable (TDS) convolution blocks and unidirectional recurrent layers in a novel way to facilitate lower latency, without degrading the performance of the source extraction module. This is the first real-time sound enhancement solution that uses a mixture of causal TDS convolution blocks and unidirectional RNN layers in the domain of real-time frame-in frame-out personalized sound enhancement.

To achieve the goal of finding a small model, in terms of overall parameter size, as well as supporting real-time operations with very small latency, at the same time without compromising on the enhancement quality, is a non-trivial technical problem. The present techniques are advantageous because they provide a solution that simultaneously meets all the criteria (i.e. the technical challenges mentioned above).

Advantages of the present techniques include:
The use of causal grouped convolutions to decrease the amount of parameters in the model by a factor of <NUM> to <NUM> times.

The use of an encoder/decoder architecture, where speaker embeddings are used not only after the end of the encoder and before the start of the decoder, but instead both at the end of the encoder and the end of the decoder. State-of-the-art literature indicates one place conditioning on bottleneck layers is enough for effective solutions, but the present applicant demonstrates this is not the case for lightweight models.

The use of shallow and therefore lightweight unidirectional RNNs as an effective means to reduce the kernel size as well as the dilations used in the grouped convolutions. This enables over 10x compression in model size to be achieved (making the model suitable for implementation on resource-constrained devices).

Finally, although the ultimate goal is to create enhancement models that have effective inference with low latency and small model size, the proposed solution is as fast to train as the state of the art in terms of training time offered by dual path solutions, which makes the optimization of such models, such as hyper-parameter tuning or neural architecture search related technologies more scalable to tackle on.

As explained below in more detail, the main contributions of the present techniques include PSE-Net - a fully streamable, on-device personalised sound enhancement network, which is designed with TDS cells and recurrent layers to meet memory and latency requirements. Extensive evaluation of the proposed PSE-Net model is presented by measuring SDRi and WER performances when used in conjunction with an open source ASR system. Furthermore, language shift is analysed for models trained on English and evaluated (for SDRi and WER) on Spanish, German, and English. This sheds light on how sound enhancement systems trained on high resource public data are able to transfer to unseen structurally different languages.

Architecture. <FIG> is a schematic diagram showing the architecture of the machine learning, ML, model of the present techniques. The ML model (also referred to herein as "PSE-Net") accepts (i) a noisy spectrogram and (ii) speaker-embedding of a target user as inputs and outputs a time-frequency mask, which is then applied on the input audio spectrogram to obtain the enhanced spectrogram. As shown in <FIG>, the model comprises an encoder and decoder style network with intermediate filter blocks. Finally, there is a fully-connected layer with sigmoid activations to generate the final output mask.

Encoder and Decoder. Both encoder and decoder are composed of four time-depth-separable convolution (TDS) cells. A TDS cell (as shown on top right of <FIG>) starts with <NUM>-D convolution that operates an input tensor of shape T x F and produces an output tensor of the same shape. Here, T is the number of time-steps and F is the number of channels, i.e., features. Scaled Exponential Linear Unit (SeLU) is used as the activation of the convolution layer, which alleviates the issue of dead nodes during back propagation. Finally, residual connections are added and layer normalization is applied. The output is then passed through a fully-connected layer with F units, such that the output shape of the cell remains identical to the input shape. The output from the fully connected layer is added with a residual connection and a layer normalization is applied.

A kernel size of <NUM> and <NUM> groups is used in all cells, which helps in significantly reducing the network size. In order to obtain the same output shape from <NUM>-D convolution without any future look-ahead, left-padding is performed using the very first frame of the input. For example, if the input frames are {t1, t2,. , tn}, they are padded with six frames on the left as follows: {t1, t1, t1, t1, t1, t1, t1, t2,.

Filter Block. A filter block with N recurrent layers is designed, with a residual connection added at the end. Before passing the input to the filter block, each time frame is concatenated with a vector representing the speech profile of the target user. When the speech profile of the target user is known, each time frame is concatenated with a speaker embedding vector; when the speech profile of the target user is not known, each time frame is concatenated with a zero vector. It is assumed that the speaker embedding vector is pre-computed using a speech recognition model, such as an X-Vector model, from utterances of a target user during an enrolment phase, and thus no additional computations are needed during the sound extraction phase.

Model training / inference pipeline. To improve the generalizability of the sound enhancement model, a data augmentation technique covering two noise types is introduced: (a) interfering utterance from a different speaker, i.e., babble noise, and (b) common non-speech environmental noise. The noise signals are mixed with the clean audio to reflect four common scenarios with interfering signal: (i) present throughout target utterance, (ii) occurring briefly within the target and corrupting a subsegment, (iii) starting before the target utterance but stopping before target utterance completion and (iv) vice-versa. <FIG> shows these scenarios. Lastly, interference augmentation is made stochastic by scaling the interference based on a target signal-to-noise (SNR) level chosen randomly for each utterance during the training phase. In addition to the corrupted input, the data pipeline of the present techniques also stochastically includes clean input (without any corruption) to allow the network to learn pass-through behaviour.

<FIG> is a block diagram of the mechanism for data augmentation for ML model training. The diagram shows the data augmentation technique used to help achieve generalizable sound enhancement model.

<FIG> is a schematic diagram showing ML model training and the use of the trained ML model. In other words, <FIG> shows the detailed pipeline for training and inference phases.

<FIG> is a flowchart of example steps for training an ML model to perform real-time personalised or non-personalised sound enhancement for a target user. The steps shown in <FIG> may be performed off-device (i.e. off of the end user device where the trained ML model is deployed), such as on a remote server.

The method comprises obtaining a training dataset comprising a plurality of audio noise samples, and a plurality of clean audio samples that each contain speech of individual speakers, and a speaker embedding vector for each individual speaker (step S102). The clean audio samples may be captured from a plurality of speakers. The clean audio samples x ∈ R(t+<NUM>) may be captured in the time domain, and may first be converted to the frequency domain z=ρ⊙eiϕ, where <MAT>.

The method comprises generating, using the clean audio samples, corrupted audio samples by adding at least one noise sample to each clean audio sample (step S102). The generated corrupted samples x̃ ∈ Rt*<NUM>(i.e., clean sample augmented with noise) may also be converted to the frequency domain z̃. As the ML model of the present techniques enhances only the magnitude information ρ̃, the enhanced signal obtained is therefore <MAT>, where <MAT> denotes the causal frame-in frame-out PSE-Net, which represents magnitude based frequency masking. Note that when applying a Short-Time Fourier Transform (STFT), a window length of <NUM> and a stride of <NUM> may be used.

Step S102 to generate corrupted audio samples may comprise entirely overlaying the speech in a clean audio sample with an audio noise sample. Additionally or alternatively, generating the corrupted audio samples may comprise partially overlaying the speech in a clean audio sample with an audio noise sample.

The method may comprise training neural networks of the ML model, using the training dataset and the corrupted audio samples, to remove the noise from the corrupted audio samples while maintaining the speech of the individual speakers, and to learn to switch between performing personalised and non-personalised noise removal depending on whether the speaker embedding vector for an individual speaker is available during a training round (step S104). The model may be optimised with an objective to minimise the differences between the clean signal z and enhanced signal ẑ. This could be achieved by minimising the discrepancies between the clean magnitude ρ and the enhance magnitued <MAT>, i.e., the Mean Squared Error (MSE):
<MAT>.

It has been found that, if rather than magnitudes the normalized magnitudes are operated on in (<NUM>,<NUM>), then convergence speed can be increased if instead a power in the MSE is minimised, namely:
<MAT>
for α varying during training according to a decreasing scheduler from αbegin to αend. The intuition is that for normalised magnitudes, a higher value of α will force the training to focus on decreasing larger discrepancies, whereas a lower value of α will encourage the training to decrease large and small errors similarly. This shift in focusing on more important rather than generic errors, increased convergence speed substantially which was an important factor in scaling the hyper parameter optimization experiments.

Step S104 to train neural networks of the ML model may comprise: inputting the corrupted audio sample into an encoder module of the ML model; concatenating a vector with each frame of the corrupted audio sample after processing by the encoder module, to generate a modified corrupted audio sample (as shown in <FIG>); inputting the modified corrupted audio sample into a decoder module of the trained ML model; and concatenating the vector with each frame of the modified corrupted audio signal after processing by the decoder module (as shown in <FIG>), to output an enhanced audio sample.

When the speaker embedding vector exists during a training round, the vector is the speaker embedding vector and the ML model switches to perform personalised noise removal. In this case, the model learns to remove ambient noise and/or babble noise from the output enhanced audio sample, while maintaining the speech of the target user.

Alternatively, when no speaker embedding vector exists, the vector is a zero vector and the ML model switches to perform non-personalised noise removal. In this case, the model learns to remove only ambient noise from the output enhanced audio sample, while maintaining the speech of the target user.

Step S104 to train neural networks of the ML model may comprise: comparing each outputted enhanced audio sample with the corresponding clean audio sample and determining how well the outputted enhanced audio sample matches the corresponding clean audio sample; determining, using a result of the comparing, a loss function for training the neural networks; and updating the neural networks to minimise the loss function.

Additionally or alternatively, step S104 to train neural networks of the ML model may comprise: applying an automatic speech recognition model to each clean audio sample to obtain a transcript of the speech; applying the automatic speech recognition model to each outputted enhanced audio sample to obtain a transcript of the speech; comparing the transcript of the speech from the outputted enhanced audio sample with the transcript of the speech from the corresponding clean audio sample and determining how well the transcript from the outputted enhanced audio sample matches the transcript from the corresponding clean audio sample; determining, using a result of the comparing, a loss function for training the neural networks; and updating the neural networks to minimise the loss function. Preferably, determining a loss function comprises determining a word-error-rate for the transcript of the speech from the generated enhanced audio sample.

The method may further comprise transmitting the trained (pre-trained) ML model to at least one user electronic device for use (not shown in <FIG>).

<FIG> is a flowchart of example steps for using the trained ML model on-device to perform real-time sound enhancement for a target user. The method may comprise: obtaining a noisy audio signal comprising speech of a target user and noise (step S300).

The method may comprise determining whether a speaker embedding vector for the target user exists (step S302). As mentioned above, the presence of a speaker embedding vector determines whether the sound enhancement performed by the ML model is personalised or non-personalised.

The method may comprise using neural networks of the trained ML model, to remove the noise from the noisy audio signal while maintaining the speech of the target user by switching the trained ML model to perform personalised noise removal when the speaker embedding exists (step S304) or to perform non-personalised noise removal when no speaker embedding vector exists (step S306).

Using the trained ML model to perform personalised noise removal (step S304) may comprise: inputting the corrupted audio signal into an encoder module of the trained ML model; concatenating a speaker embedding vector with each frame of the noisy audio signal after processing by the encoder module, to generate a modified noisy audio signal; inputting the modified noisy audio signal into a decoder module of the trained ML model; and concatenating the speaker embedding vector with each frame of the modified noisy audio signal after processing by the decoder module, to output an enhanced audio signal. In this case, ambient (environmental) noise and/or babble noise is removed from the output enhanced audio signal, while maintaining the speech of the target user.

Using the trained ML model to perform non-personalised noise removal (step S304) may comprise: inputting the corrupted audio signal into an encoder module of the trained ML model; concatenating a zero vector with each frame of the noisy audio signal after processing by the encoder module, to generate a modified noisy audio signal; inputting the modified noisy audio signal into a decoder module of the trained ML model; and concatenating the zero vector with each frame of the modified noisy audio signal after processing by the decoder module, to output an enhanced audio signal. In this case, only ambient noise is removed from the output enhanced audio signal, while maintaining the speech of the target user.

The corrupted audio signal may be obtained during an audio call. The method may further comprise transmitting the audio signal after processing by the trained ML model to another participant in the audio call. Thus, the 'cleaned-up' audio signal is transmitted to the listener(s) in the audio call (instead of the noisy signal), thereby improving the sound quality of the audio call for the listener(s). It will be understood that the noisy signal could equally be obtained during an audio call from another participant in the audio call, and in this case, the noisy signal may be cleaned/denoised before the user hears the audio signal. Thus, as noted above, in the context of an audio call, the noisy audio signal that is enhanced in real-time may be the audio signal of the user that is sent to another participant or is the audio signal of the other participant that is sent to the user.

The method may further comprise inputting the audio signal after processing by the trained ML model into an automatic speech recognition, ASR, system. Thus, the 'cleaned-up' audio signal may be easier to process by the ASR system and may result in a lower word error rate.

<FIG> is a block diagram of a system <NUM> for training a machine learning, ML, model to perform real-time sound enhancement for a target user.

The system <NUM> comprises a server <NUM> arranged to perform the steps described above with reference to <FIG> to generate a trained ML model.

The system <NUM> comprises an apparatus <NUM> used to implement the trained ML model. The apparatus <NUM> may be any one of: a smartphone, tablet, laptop, computer or computing device, virtual assistant device, a vehicle, a drone, an autonomous vehicle, a robot or robotic device, a robotic assistant, image capture system or device, an augmented reality system or device, a virtual reality system or device, a gaming system, an Internet of Things device, or a smart consumer device (such as a smart fridge). It will be understood that this is a non-exhaustive and non-limiting list of example apparatus.

The server <NUM> is communicatively coupled to the apparatus <NUM>, and is able to transmit the trained ML model to the apparatus <NUM>.

The apparatus <NUM> comprises at least one processor <NUM> coupled to memory <NUM>. The at least one processor <NUM> may comprise one or more of: a microprocessor, a microcontroller, and an integrated circuit. The memory <NUM> may comprise volatile memory, such as random access memory (RAM), for use as temporary memory, and/or nonvolatile memory such as Flash, read only memory (ROM), or electrically erasable programmable ROM (EEPROM), for storing data, programs, or instructions, for example.

The apparatus <NUM> may comprise storage <NUM> which may store a trained ML model <NUM>. The trained ML model <NUM> is the model obtained from the server <NUM>.

The apparatus <NUM> may comprise an audio capture device <NUM> for capturing sound/ audio signals which are to be processed by the trained ML model <NUM>. The apparatus <NUM> may comprise an interface <NUM> (e.g. a communication interface) for transmitting audio signals after they have been processed by the trained ML model <NUM>. For example, an noisy audio signal may be captured by the audio capture device <NUM> during an audio call made using the apparatus. The processor may be arranged to transmit the audio signal after processing by the trained ML model to another participant (not shown) in the audio call. Similarly, interface <NUM> may be able to receive audio signals obtained from another participant in an audio call. The trained ML model may be able to enhance noisy audio signals received from another participant in an audio call, as described above. This may enable the user of apparatus <NUM> to hear enhanced versions of the noisy audio signals received from the other participant in the audio call, thereby improving the audio quality of the sound the user of apparatus <NUM> hears.

The at least one processor <NUM>, coupled to memory <NUM>, may be arranged to: obtain, from the audio capture device <NUM>, a noisy audio signal comprising speech of a target user and noise; determine whether a speaker embedding vector for the target user exists; and use neural networks of the trained ML model <NUM>, to remove the noise from the noisy audio signal while maintaining the speech of the target user by switching the trained ML model <NUM> to perform personalised or non-personalised noise removal depending on whether the speaker embedding vector exists.

Integration with ASR: Publicly available pre-trained enterprise-grade automatic speech recognition (ASR) models are used, which are available for English, German and Spanish languages ("Silero models: pre-trained enterprise-grade stt / tts models and benchmarks," https://github. com/snakers4/silero-models, <NUM>). After enhancing the frequency-domain signal magnitude with PSE-Net, the output is converted to the time-domain, using the corrupted frequency-domain signal phase, and it is then passed through the ASR models. The output of the ASR models is then compared with the ground-truth transcripts to compute the word error rate (WER) results.

Thus, as mentioned above, training neural networks of the ML model may comprise: applying an automatic speech recognition model to each reference audio sample to obtain a transcript of the speech; applying the automatic speech recognition model to each generated enhanced audio sample to obtain a transcript of the speech; comparing the transcript of the speech from the generated enhanced audio sample with the transcript of the speech from the corresponding reference audio sample and determining how well the transcript from the generated enhanced audio sample matches the transcript from the corresponding reference audio sample; and determining, using a result of the comparing, a loss function for training the neural networks.

Dataset: The training dataset used to train the ML model of the present techniques is constructed from LibriSpeech (V. Panayotov, G. Povey, and S. Khudanpur, "Librispeech: an ASR corpus based on public domain audio books," in ICASSP, <NUM>), <NUM> and <NUM> training splits, which contain clean speech. Since in addition to speech and text, the LibriSpeech dataset contains speaker identifier, tuples (r,z,z̃) are built, where r, z, and z̃ respectively denote reference, clean and corrupted signal of a speaker. To evaluate the PSE-Net model with ASR integration, test-clean split of LibriSpeech is used for English ASR model. For Spanish and German ASR models, test-split of VoxForge dataset is used, which is equivalent to <NUM>% split ensuring each user's data is only in one split ("VoxForge Corpus," http://www. org, <NUM>).

The proposed causal frame-in frame-out PSE-Net model was compared against the state-of-the-art (SOTA) causal frame-in frame-out VoiceFilter-Lite model (Q. Pelecanos, M. Nika, and A. Gruenstein, "VoiceFilter-Lite: Streaming Targeted Voice Separation for On-Device Speech Recognition," in Interspeech, <NUM>). Several VoiceFilter-Lite model variations were trained and the model yielding the best signal-to-distortion ratio improvement (SDRi) and WER results was chosen for the comparison experiments, even though the model choice exceeds the memory limitations for embedded deployment. In addition, results of SOTA in non-causal model is presented, namely the VoiceFilter (VF) model (Q. Muckenhim, K. Sridhar, Z. Hershey, R. Saurous, R. Jia, and I. Moreno, "VoiceFilter: Targeted Voice Separation by Speaker-Conditioned Spectrogram Masking," in Interspeech, <NUM>) to compare the real-time results of PSE-Net with a strong non-realtime baseline. As the representative audio degradation schemes, in the experiments presented here, only consider additive babble and ambient noise are considered.

<FIG> is a table showing SDRi results for the trained ML model and baseline models assessed across different languages, under ambient noise and babble. As an imperfect replacement for perceptual audio quality metric, the Signal-to-Distortion Ratio improvement (SDRi) metric is used to measure the objective quality of the enhanced signal. As shown in <FIG> (under the "English" column), all three top performing PSE-Net models outperform SOTA VoiceFilter-Lite model for enhancing speech corrupted with both noise types. Specifically, the best PSE-Net model (highlighted in bold) achieves <NUM> and <NUM> SDRi for suppressing babble and ambient noises respectively. Additionally, it is shown that the performance of PSE-Net is close to the VoiceFilter model. Moreover, the SDRi is overall better (i.e., higher) for denoising ambient noise compared to babble noise. This is in-line with previous studies which have shown that it is difficult to suppress babble noise compared to ambient noise. It has been found, by searching for different parameters, that increasing model complexity does not necessarily lead to better model performance.

The inference latency of PSE-Net was also measured on NVIDIA Jetson Nano as a representative mobile platform and NVIDIA GeForce GTX <NUM> Ti for a non-embedded comparison. It was found that the three PSE-Nets perform speech enhancement with a RT-factor of <NUM>-<NUM>. 84x on Jetson Nano, and <NUM>-<NUM>. 27x on GeForce GTX <NUM> Ti. All PSE-Net models have <NUM>-<NUM> parameters.

<FIG> is a table showing the impact of the models of <FIG> on word error rate in automatic speech recognition tasks. To quantify the effectiveness of PSE-Net on downstream ASR tasks, the improvement in word-error-rate (WER) was measured when passing the enhanced speech, as opposed to the corrupted speech, through an ASR model. The open source Silero ASR models mentioned above were used for three languages and WER comparison results are presented in <FIG> under clean, babble and ambient noise conditions. The results show that all PSE-Net variants can effectively suppress both babble and ambient noise, and outperform SOTA VoiceFilter-Lite model with a significant margin. In particular, it can be seen that PSE-Net models perform close to VoiceFilter (non-causal) model within a <NUM>% WER absolute difference, which is <NUM>. 5x better compared to VoiceFilter-Lite incase of babble noise. Similarly, for ambient noise there is a <NUM>. 7x improvement with PSE-Net over VoiceFilter-Lite and only a <NUM>% WER absolute different to VoiceFilter. It can be seen that the model achieving best SDRi is also the best for downstream ASR task. However, no significant correlation has been found between the SDRi and WER metrics. Overall, the best PSE-Net model achieves <NUM>% and <NUM>% absolute improvement in WER for English under babble and ambient noise. The results also show a negligible hit on the ASR WER when enhancing a clean signal (i.e. reference audio sample), and this oversupression is inline with the findings of existing studies.

The performance of speech enhancement models trained on English, but applied to other languages, was also investigated. Specifically,the performance of PSE-Net, which has been trained using English audio speech samples, was evaluated on noisy Spanish and German speech signals. <FIG> show plots of SDRi and word error rates (WER) for Spanish and German audio that has been passed through speech enhancement models trained on English speech. The plots on the left-hand side in <FIG> show the results for babble suppression, while the plots on the right-hand side show the results for ambient noise suppression. It can be seen that the PSE-Net trained on English performs well when used for enhancing Spanish and German speech. This is encouraging from a practical point of view, since it highlights the potential of applying speech enhancement in a language-agnostic manner. Specifically, with Spanish dataset, <NUM>-<NUM> and <NUM>-<NUM> SDRi is observed after enhancing signals corrupted with babble and ambient noises. With German dataset, around <NUM> and <NUM>-<NUM> SDRi is observed after suppressing babble and ambient noises. Overall, the results show that there is a significantly large improvement over SOTA VoiceFilter-Lite model, even when both models are trained with similar settings.

In case of ASR, the presence of babble noise has a much bigger impact, and with PSE-Net the ASR performance could be significantly improved in such conditions. Specifically, a <NUM>-<NUM>% and <NUM>-<NUM>% absolute improvement is observed in WER for Spanish and German languages. Overall, the results show PSE-Net has great potential for tranferability of personalized speech enhancement across languages. Compared to the VoiceFilter-Lite model, which fails to perform effectively on languages other than what it was trained for, PSE-Net shows a great potential for successfully transferring speech enhancement system across languages. Furthermore, it is observed that PSE-Net performs similarly to the non-causal VoiceFilter model, specially for babble noise.

In <FIG>, the correlation between English speech enhancement models being applied to Spanish and German languages for SDR and WER improvements is shown. The top seven PSE-Net models (from all models trained during hyper-parameter optimisation) were selected for this analysis, as well as the VoiceFilter-Lite and the VoiceFilter model. The results show there is a preservation of the model ranking in terms of both SDRi and WER, when applied to Spanish and German. Additionally, the various PSE-Net architectures offer different performance/computation budgets decreasing the gap to non-causal VoiceFilter.

Note the performance of PSE-Net is not perfect when transferred to other languages. This could be due to the fact that VoxForge dataset splits are leveraged for Spanish and German, and the recordings for German dataset are somewhat far-field, whereas Spanish recordings are mainly close-field. This provides an additional dimension to the analysis, which should favour transfer to close-field given the present speech enhancement models are trained on LibriSpeech dataset which is mainly close-field. Indeed, <FIG> and <FIG> capture this phenomenon which might also be due to variability of voice characteristics between native German speaking people than native Spanish speaking people.

<FIG> is a block diagram illustrating how the ML model switches between performing personalised and non-personalised sound enhancement. As explained above, there are two inputs into the ML model that performs speech enhancement (both during training time and during inference time). If a speaker embedding vector for a particular user exists, then the speaker embedding vector and the corrupted speech (noisy audio signal) is input into the ML model; and if no such speaker embedding vector exists, then a zero vector and the corrupted speech is input into the ML model. Whether the speaker embedding vector exists depends on whether a user has provided enrolment data, i.e. has taken part in an enrolment process to provide a sample of their voice (i.e. a clean audio signal containing just the user's voice). This enrolment process only needs to be performed once per user. It is possible to use the trained ML model to perform sound enhancement before this enrolment process is performed because, as shown in <FIG>, the ML model simply switches to performing non-personalised sound enhancement instead.

<FIG> is a schematic diagram showing how the architecture illustrated in <FIG> is used to perform personalised and non-personalised sound enhancement. As explained above, when the speaker embedding vector exists (left hand side), the trained ML model is switched to perform personalised noise removal and, in this case, using neural networks of the trained ML model to remove the noise comprises: inputting the noisy audio signal into an encoder module of the trained ML model; concatenating the speaker embedding vector with each frame of the noisy audio signal after processing by the encoder module, to generate a modified noisy audio signal; inputting the modified noisy audio signal into a decoder module of the trained ML model; and concatenating the speaker embedding vector with each frame of the modified noisy audio signal after processing by the decoder module, to output an enhanced audio signal.

Similarly, when no speaker embedding vector exists (right hand side), the trained ML model is switched to perform non-personalised noise removal and, in this case, using neural networks of the trained ML model to remove the noise comprises: inputting the noisy audio signal into an encoder module of the trained ML model; concatenating a zero vector with each frame of the noisy audio signal after processing by the encoder module, to generate a modified corrupted audio signal; inputting the modified noisy audio signal into a decoder module of the trained ML model; and concatenating the zero vector with each frame of the modified noisy audio signal after processing by the decoder module, to output an enhanced audio signal.

The speaker embedding vector of a target user supplies the key information pertaining to personalisation of the sound enhancement performed by the ML model. The impact of the speaker embedding vector on personalisation was investigated. The amount of enrolment data needed to perform personalisation was also investigated.

<FIG> is a table showing the contribution of a not having a speaker embedding vector for a target user on the performance of the ML model at removing ambient noise from an audio signal. The results presented in this table capture the impact in word-error-rate (WER) and Signal-to-Distortion Ratio (SDR), when the speaker embedding vectors are replaced by unit, zero or random vectors. From the results it can be seen that even with the use of unit or zero vectors as the speaker embedding vectors, the performance of the ML model on removing ambient noise is only marginally impacted. This means that the ML model is able to remove ambient noise when no speaker embedding vector for a target user exists. This concurs with hypotheses that the encoder and decoder parts of the network (see <FIG> and <FIG>) filter out the non-speech noises. The filter blocks which receive the speaker embedding as inputs handle the separation of the target speech from interfering speech and perform poorly on babble noise when presented with non-target embedding vectors.

<FIG> is a table showing the impact of different lengths of enrolment data on the performance of the ML model at removing different types of noise from an audio signal. The results presented in this table summarise the impact of the quantity of enrolment data used in the computation of speaker embedding vectors in the overall performance of the ML model. It can be seen that the ML model performs well even with just one second of enrolment data. Overall, it can be seen that even without any enrolment data, the ML model of the present techniques can suppress ambient noise.

Thus, the present techniques provide a causal model for real-time enhancement of speech signals by suppressing the background noise. The Applicant has shown that the proposed approach outperforms current SOTA solution for streaming-based sound enhancement to suppress both non-stationary and babble noise. Moreover, it has been demonstrated that the capability of the present speech enhancement model transfers well across languages.

Claim 1:
A computer-implemented method for using a trained machine learning, ML, model to perform real-time sound enhancement for a target user, the method comprising:
obtaining a noisy audio signal comprising speech of a target user and noise;
determining whether a speaker embedding vector for the target user exists; and
using neural networks of the trained ML model, to remove the noise from the noisy audio signal while maintaining the speech of the target user by switching the trained ML model to perform personalised or non-personalised noise removal depending on whether the speaker embedding vector exists, by:
inputting the noisy audio signal into an encoder module of the trained ML model;
concatenating a vector with each frame of the noisy audio signal after processing by the encoder module, to generate a modified noisy audio signal;
inputting the modified noisy audio signal into a decoder module of the trained ML model; and
concatenating the vector with each frame of the modified noisy audio signal after processing by the decoder module, to output an enhanced audio signal,
wherein:
when the speaker embedding vector exists, the vector is the speaker embedding vector and the trained ML model is switched to perform personalised noise removal, and
when no speaker embedding vector exists, the vector is a zero vector and the trained ML model is switched to perform non-personalised noise removal.